We live in an age of evidence-based healthcare, where the concept of evidence has been avidly and often uncritically embraced as a symbol of legitimacy, truth, and justice. By letting the evidence dictate healthcare decision making from the bedside to the policy level, the normative claims that inform decision making appear to be negotiated fairly—without subjectivity, prejudice, or bias. Thus, the term ‘‘evidence-based’’ is typically read in the health sciences as the empirically adequate (...) standard of reasonable practice and a means for increasing certainty. Supporters believe that evidence-based medicine (EBM) can introduce rational order to the deliberative processes of healthcare decision making. It is perhaps puzzling, then, to come across critical perspectives (typically arising from the humanities and the more theory-driven social sciences) raising concerns about a seeming technogovernance being introduced by this deferral to the evidence where power interests can be obfuscated by way of technical resolve. The critics holding this minority view argue that technological solutions to problems of knowledge and practice cannot replace medicine’s normative content. Against EBM’s democratic leanings toward transparency and accountability, medical criteria alone cannot decide valueladen ethically charged decisions. This paper attempts to explain and evaluate this important debate in the philosophy of medicine, focusing specifically on the dispute over 'evidence-based women's health'. (shrink)

Background: The World Health Organization has endorsed a community-based health insurance scheme (CBHIS) as a shared financing plan to improve access to health services and ensure universal coverage of the healthcare delivery system. Such a contributory scheme is the most likely option to provide health insurance coverage when governments cannot offer direct health care support. Despite improvements in access to current healthcare services, Ethiopia’s healthcare delivery remained low, owing to the country’s underdeveloped healthcare finance (...) system. As a result, the present study assessed CBHIS coverage and its predictors in Ethiopia at the individual and community level. Methods: The availability of CBHIS was checked via a criterion: at least one of the cluster respondents had to be enrolled in CBHIS. This study was based on secondary data from the Ethiopia Mini Demography and Health Survey (EMDHS) 2019 and included 7724 respondents. The study population was described using percentage and frequency. Four multilevel mixed-effects logistic regression modelling stages were performed to control for variations due to heterogeneity across clusters, and determinant predictors of CBHIS enrollment were outplayed. Results: The prevalence of CBHIS enrollment in Ethiopia was 33.13%. Rural residents were 3.218 times (AOR = 3.218; 95% CI: 1.521, 6.809), male household heads were 1.574 times (AOR = 1.574, 95% CI: 1.105, 2.241), getting funds from the safety net program were times 2.062 (AOR = 2.062, 95% CI: 1.297, 3.279), attending the primary educational level was 1.686 times (AOR = 1.686, 95% CI: 1.007, 2.821), bank accounts were 1.373 times (AOR = 1.373, 95% CI: 1.052, 1.792), and wealth index was 1.356 times (AOR = 1.356, 95% CI: 1.001, 1.838) more likely associated with CBHIS coverage, whereas the regions, the other religions, and women aged 20– 24 had lower odds of CBHIS coverage. Conclusion: In Ethiopia, regional healthcare expenditure per capital, religious affiliation, women age range, residents, sex of household head, funds from the safety net program, formal educational level, and having bank accounts were associated with community-based health insurance scheme coverage. (shrink)

The study aimed at identifying responsive flexibility and its role in improving service quality, from the point of view of the internal beneficiary in non-governmental hospitals in Gaza Strip. The study relied on the descriptive and analytical approach, and the questionnaire was designed as a tool to collect data, and the researchers used the comprehensive survey method, and the number of the study population was (536) single, where (434) questionnaires were retrieved, and the recovery rate was (80.97%). The study showed (...) many results, the most important of which were: The presence of moderate degree of approval by the study sample on the responsive flexibility, as it was evident through the response flexibility field obtaining a relative weight (60.50%), and the results of the study indicated that there is a high degree of approval by the members of the study sample. On the quality of service, as it was evident through the field of service quality obtaining a relative weight (79.90%). The results of the study revealed a statistically significant relationship between responsive flexibility and service quality in non-governmental hospitals in Gaza Strip, with a correlation coefficient of 0.418, and the results of the study revealed a statistically significant effect between responsive flexibility and service quality in non-governmental hospitals in Gaza Strip. The study reached many recommendations, the most important of which were: Finding mechanisms by which stored information can be used to enhance the decision-making process, and setting up an effective system to receive patients' complaints in a manner that ensures rapid response and treatment, to achieve continuous communication between patients and the hospital management, and to notify patients of abuse With the complaints that they submit, and working to provide all medical and health specialties in the hospitals under study, by making use of the medical delegations that visit Gaza Strip, involving them in the treatment processes, bringing in doctors and specialists from abroad, and updating the standards related to measuring the services provided to patients continuously, based on On patients' suggestions and complaints, developing facilities in hospitals, as well as updating medical devices and equipment used in hospitals periodically. (shrink)

Abstract: The study aimed to determine the strategic flexibility and its relationship to the level of quality of services provided, from the viewpoint of the internal beneficiary in non-governmental hospitals in Gaza Strip. The study relied on the descriptive and analytical approach, and the questionnaire was designed as a tool to collect data and consisted of (39) items, and the researchers used the comprehensive survey method, and the number of the study population was (536) individuals, where (434) questionnaires were retrieved, (...) and the recovery rate was (80.97%). The study revealed many results, the most important of which were: the existence of a moderate degree of approval by the study sample individuals on strategic flexibility, as it was evident through the area of strategic flexibility as a whole having a relative weight (60.44%). The study is on the quality of services, as it became clear through the field of service quality obtaining a relative weight (79.90%). The results of the study revealed a statistically significant relationship between strategic flexibility and the quality of services in non-governmental hospitals in Gaza Strip, with a correlation coefficient of 0.490. The study reached many recommendations, the most important of which were: the need to work on appointing young people and those with potentials, because jobs are vacant in the hospitals under study, and the need to seek the help of an administrative staff with scientific and practical qualifications, and to work on updating information systems, archiving and networks through which data and information are transferred between departments And the creation of mechanisms by which stored information can be used to enhance the decision-making process, and an effective system to receive patients' complaints in a manner that ensures rapid response and treatment, to achieve continuous communication between patients and the hospital management, and to notify patients of dealing with the complaints they submit, and work to provide all Medical and health specialties in the hospitals under study, by making use of the medical delegations that visit Gaza Strip, involving them in the treatment processes, bringing in doctors and specialists from abroad, updating the standards related to measuring the services provided to patients on an ongoing basis, based on the suggestions and complaints of patients, and developing facilities in hospitals As well as updating the medical devices and equipment used in hospitals periodically. (shrink)

Abstract: The study aimed to determine the quality of services and its role in enhancing strategic flexibility, from the point of view of the internal beneficiary in non-governmental hospitals in Gaza Strip. The study relied on the descriptive and analytical approach, and the questionnaire was designed as a tool to collect data and consisted of (39) items, and the researchers used the comprehensive survey method, and the number of the study population was (536) individuals, where (434) questionnaires were retrieved, and (...) the recovery rate was (80.97%). The study revealed many results, the most important of which were: the existence of a high degree of approval by the study sample regarding the quality of services, as it was evident through the field of service quality obtaining a relative weight (79.90%), and the results of the study indicated that there is a moderate degree of approval by the members of the study sample. On the strategic flexibility, as it was evident by obtaining the area of strategic flexibility as a whole on a relative weight (60.44%). The results of the study revealed a statistically significant relationship between the quality of services and the strategic flexibility in non-governmental hospitals in Gaza Strip, with a correlation coefficient of 0.490). The results of the study also revealed a statistically significant effect of the quality of services on the strategic flexibility in non-governmental hospitals in Gaza Strip. The study reached many recommendations, the most important of which were: Establishing an effective system to receive patients' complaints in a manner that ensures rapid response and treatment, to achieve continuous communication between patients and the hospital administration, to notify patients of dealing with the complaints they submit, and to work to provide all medical and health specialties in hospitals in place. The study, by making use of medical delegations visiting Gaza Strip, involving them in therapeutic operations, bringing in doctors and specialists from abroad, updating the criteria for measuring services provided to patients on an ongoing basis, based on patients' suggestions and complaints, developing facilities in hospitals, as well as updating medical devices and equipment Used in hospitals periodically and the need to work on appointing young people and people of energy, for vacancies in the hospitals under study, and the need to seek the help of an administrative staff that is distinguished by scientific and practical qualifications, and to work on updating information systems, archiving and networks through which data and information are transferred between departments, and finding mechanisms to be done from During which the information stored can be used to enhance the decision-making process. (shrink)

The Coronavirus disease (COVID-19) has spread to almost all provinces in Indonesia, including South Sumatra. Epidemiological models are required to provide evidence for public health policymakers to mitigate the virus. The aim of this study is: 1) to create a prediction model for COVID-19 cases in South Sumatra to help inform about public health policy and 2) to reflect on women’s experiences to provide solutions for mitigating the impact of COVID-19. This study uses quantitative and qualitative methods. (...) A quantitative modeling approach called Susceptible–Infected–Recovered (SIR) model is used to predict COVID-19 cases in South Sumatra. The assumption used is that every four days, a doubling of COVID-19 cases is observed, with an average of 15 days for recovery. The sources of data are reports from the South Sumatra Provincial Government and the Ministry of Health of the Republic of Indonesia (MOH RI). Qualitative data are obtained through a feminist participatory action research project, which is focused on children’s experiences of COVID-19. Reflective analysis is conducted to develop insights into how to empower women with respect to mitigating COVID-19. Results show that COVID-19 cases in South Sumatra are still underreported, with only 5%–10% of the total estimated COVID-19 cases being reported. Modeling indicates that over 1,000 people had COVID-19 by the end of April, reaching over 150,000 by the end of May, and over a third of South Sumatra’s population is likely to be infected by the end of June. Multiple interventions are needed to reduce cases and flatten the curve. Women are key to flattening this curve and must be empowered to undertake actions from a familial base. (shrink)

In 1705, Mary Astell and Damaris Masham both published works advocating for women's use of individual judgment in matters of religion. Although both philosophers advocate for women's education and intellectual autonomy, and both are adherents of the Church of England, they differ dramatically in their attitudes to religious authority. These differences are rooted in a deeper disagreement about the nature of epistemic authority in general. Astell defends an interpersonal model of epistemic authority on which we properly trust testimony (...) when the testifier is answerable for its truth. Masham holds an evidence model of epistemic authority on which testimony is treated as an ordinary piece of empirical evidence. Central to Masham's argument is her contention that religious beliefs based on the kind of authority recognized by Astell could never serve as a stable source of moral motivation. Because of their different theories of epistemic authority, Masham's defense of women's intellectual autonomy leads to a radical anti-clericalism, while Astell's defense is fully consistent with her insistence on deference to the established church. (shrink)

Background: The World Health Organization has endorsed a community-based health insurance scheme (CBHIS) as a shared financing plan to improve access to health services and ensure universal coverage of the healthcare delivery system. Such a contributory scheme is the most likely option to provide health insurance coverage when governments cannot offer direct health care support. Despite improvements in access to current healthcare services, Ethiopia’s healthcare delivery remained low, owing to the country’s underdeveloped healthcare finance (...) system. As a result, the present study assessed CBHIS coverage and its predictors in Ethiopia at the individual and community level. (shrink)

This paper explores how the diagnosis of mental disorder may affect the diagnosed subject’s self-concept by supplying an account that emphasizes the influence of autobiographical and social narratives on self-understanding. It focuses primarily on the diagnoses made according to the criteria provided by the Diagnostic Statistical Manual of Mental Disorders (DSM), and suggests that the DSM diagnosis may function as a source of narrative that affects the subject’s self-concept. Engaging in this analysis by appealing to autobiographies and memoirs written by (...) people diagnosed with mental disorder, the paper concludes that a DSM diagnosis is a double-edged sword for self- concept. On the one hand, it sets the subject’s experience in an established classificatory system which can facilitate self-understanding by providing insight into subject’s condition and guiding her personal growth, as well as treatment and recovery. In this sense, the DSM diagnosis may have positive repercussions on self-development. On the other hand, however, given the DSM’s symptom-based approach and its adoption of the Biomedical Disease model, a diagnosis may force the subject to make sense of her condition divorced from other elements in her life that may be affecting her mental- health. It may lead her frame her experience only as an irreversible imbalance. This form of self-understanding may set limits on the subject’s hopes of recovery and may create impediments to her flourishing. (shrink)

An abundance of data unequivocally shows that exercise can be an effective tool in the fight against obesity and its associated co-morbidities. Indeed, physical activity can be more effective than widely-used pharmaceutical interventions. Whilst metformin reduces the incidence of diabetes by 31% (as compared with a placebo) in both men and women across different racial and ethnic groups, lifestyle intervention (including exercise) reduces the incidence by 58%. In this context, it is notable that a group of prominent medics and exercise (...) scientists recently sent a well-publicised letter to the General Medical Council and Medical Schools Council calling for the introduction of evidence-based lifestyle education into all medical curricula. The letter warns that there is a lack of understanding of the impact that exercise and nutrition can have on physical health amongst doctors. In the absence of an educational overhaul, the signatories warn that the government is likely to fail to reach its goal of preventing tens of thousands of premature deaths from heart disease and cancer by 2020. Whilst we agree with the need to address this apparent lack of understanding, we argue that the ethical justification of doing so is not limited to this broadly beneficence-based justification. There is also a justification grounded in the duty of non-maleficence, that is, the duty to avoid unreasonably harming patients. This duty requires that doctors refrain from (i) preventing patients from undergoing beneficial treatment without good reason, (ii) exposing patients to unreasonable risks and (iii) reducing the therapeutic effect of an effective medical intervention. This requires an understanding of the physical impact of exercise. (shrink)

Mental health among students is one of the major concerns amidst the pandemic. Employing a correlational design, this study investigates the relationship between emotional intelligence and mental health among 152 senior high school students. Based on the statistical analysis, the r coefficient of 0.82 indicates a high positive correlation between the variables. The p-value of 0.00, which is less than 0.05, leads to the decision to reject the null hypothesis. Hence, a significant relationship exists between emotional intelligence (...) and mental health among senior high school. Implications were discussed in the study. (shrink)

Shared decision-making involves health professionals and patients/clients working together to achieve true person-centred health care. However, this goal is infrequently realized, and most barriers are unknown. Discussion between philosophers, clinicians, and researchers can assist in confronting the epistemic and moral basis of health care, with benefits to all. The aim of this paper is to describe what shared decision-making is, discuss its necessary conditions, and develop a definition that can be used in practice to support excellence in (...) maternity care. Discussion between the authors, with backgrounds in philosophy, clinical maternity care, health care management, and maternity care research, assisted the team to confront established norms in maternity care and challenge the epistemic and moral basis of decision-making for caesarean section. The team concluded that shared decision-making must start in pregnancy and continue throughout labour and birth, with equality in discourse facilitated by the clinician. Clinicians have a duty of care for the adequacy of women's knowledge, which can only be fulfilled when relevant knowledge is offered freely and when personal beliefs and biases that may impinge on decision-making (defeaters) are disclosed. Informed consent is not shared decision-making. Key barriers include existing cultural norms of “the doctor knows best” and “patient acquiescence” that prevent defeaters being acknowledged and discussed and can lead to legal challenges, overuse of medical intervention and, in some areas, obstetric violence. Shared decision-making in maternity care can thus be defined as an enquiry by clinician and expectant woman aimed at deciding upon a course of care or none, which takes the form of a dialogue within which the clinician fulfils their duty of care to the client's knowledge by making available their complete knowledge (based on all types of evidence) and expertise, including an exposition of any relevant and recognized potential defeaters. Research to develop measurement tools is required. (shrink)

Artificial Intelligence (AI) technologies are now widely used in a variety of fields to aid with knowledge acquisition and decision-making. Health information systems, in particular, can gain the most from AI advantages. Recently, symptoms-based illness prediction research and manufacturing have grown in popularity in the healthcare business. Several scholars and organisations have expressed an interest in applying contemporary computational tools to analyse and create novel approaches for rapidly and accurately predicting illnesses. In this study, we present a paradigm (...) for assessing the efficacy of combining Machine Learning (ML) and Natural Language Processing (NLP) technologies in a disease prediction system. We scraped a disease-symptom dataset with NLP characteristics from one of the UK's most trusted National Health Service (NHS) websites as an example. In addition, we will thoroughly examine our data using symptom frequency, similarity, and clustering analysis. As a consequence, we can observe that the forecast has a high efficiency rate, but there are still some challenges to work out. (shrink)

Abstract: This research employs a Random Forest classification model to predict and assess obesity and cardiovascular disease (CVD) risk based on a comprehensive dataset collected from individuals in Mexico, Peru, and Colombia. The dataset comprises 17 attributes, including information on eating habits, physical condition, gender, age, height, and weight. The study focuses on classifying individuals into different health risk categories using machine learning algorithms. Our Random Forest model achieved remarkable performance with an accuracy, F1-score, recall, and precision all (...) reaching 97.23%. The model's success is attributed to its ability to effectively leverage attributes related to eating habits, physical condition, and demographic factors. Feature importance analysis reveals key factors contributing to accurate predictions. This paper contributes to the field of health analytics by demonstrating the effectiveness of Random Forest in predicting and categorizing health risks. The insights gained from this research have the potential to inform personalized health interventions and contribute to the advancement of precision health strategies. Furthermore, the study highlights the importance of leveraging machine learning techniques for health risk assessment in diverse populations. (shrink)

This paper analyzes the determinants of women’s representation on boards of directors based on a panel of all privately owned or listed Danish firms with at least 50 employees observed during the period 1998–2010. We focus on the directors who are not elected by the employees and test three hypotheses on female board representation that we denote the female-led hypothesis, the tokenism hypothesis, and the pipeline hypothesis, respectively. We find evidence rejecting the female-led hypothesis. Firms with a female (...) chairperson on the board of directors tend to have significantly fewer other non-employee-elected female board members. We also find clear evidence of a tokenism behavior in Danish companies. The likelihood of enlarging the share of non-employee-elected female board members is significantly smaller if one, two, or more women have sat on the board of directors. Finally, the pipeline hypothesis is partly confirmed. The relation between the female pipeline of potentially qualified directors and female directors is weaker than the similar relation for males. Our findings offer insights to policy makers interested in promoting gender diversity within boardrooms. Our empirical evidence suggests that an important way to increase the female proportion of non-employee-elected board members is that more women reach top executive positions. (shrink)

Two diametrically opposed assumptions have influenced interpretations of circumcision rituals in ancient Judaism: either women performed the operation on their infant sons because children at birth and during infancy remained under the purview of the mother; or, conversely, men—specifically a ritual agent known as the mohel—performed circumcisions, because only they were typically granted authority to carry out the ritual. This study reassesses the pertinent texts, including Exodus 4 and passages from the books of Maccabees and the Babylonian Talmud (b. Šabb. (...) 134a; b. Yebam. 64b; b. ʿAbod. Zar. 27a), to determine whether women in ancient Judaism may have circumcised their infant sons; and shows that an older, family-based ritual practice in which either mothers or fathers performed the operation was being replaced by late antiquity by a specialist-based ritual performed by mohels from outside the household. (shrink)

Water quality assessment is critical for environmental sustainability and public health. This research employs neural networks to predict water quality, utilizing a dataset of 21 diverse features, including metals, chemicals, and biological indicators. With 8000 samples, our neural network model, consisting of four layers, achieved an impressive 94.22% accuracy with an average error of 0.031. Feature importance analysis revealed arsenic, perchlorate, cadmium, and others as pivotal factors in water quality prediction. This study offers a valuable contribution to enhancing water (...) quality monitoring and decision-making for stakeholders and policymakers. (shrink)

Diabetes is a major public health issue that affects the nations of our time to a large extent and is described as a non-communicable epidemic. Diabetes mellitus is a common disease where there is too much sugar (glucose) floating around in your blood. This occurs because either the pancreas can’t produce enough insulin or the cells in body have become resistant to insulin. The concentration in this paper is on diagnosis diabetes by designing a proposed expert system. The main (...) goal of this expert system is to get the appropriate diagnosing of the illness, dealing with it quickly, and tips for permanent treatment whenever possible is given out. SL5 object expert system language was used for designing and implementing the proposed expert system. (shrink)

Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with (...) a heart disease or not using the medical features of the person. We used many different algorithms of machine learning such as Gaussian Mixture, Nearest Centroid, MultinomialNB, Logistic RegressionCV, Linear SVC, Linear Discriminant Analysis, SGD Classifier, Extra Tree Classifier, Calibrated ClassifierCV, Quadratic Discriminant Analysis, GaussianNB, Random Forest Classifier, ComplementNB, MLP Classifier, BernoulliNB, Bagging Classifier, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Gradient Boosting Classifier, Decision Tree Classifier, and Deep Learning to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of heart diseases in any person. The strength of the proposed model was very satisfying and was able to predict evidence of having a heart disease in a particular person by using Deep Learning and Random Forest Classifier which showed a good accuracy in comparison to the other used classifiers. The proposed heart disease prediction model will enhances medical care and reduces the cost. This study gives us significant knowledge that can help us predict the person with heart disease. The dataset was collected from Kaggle depository and the model is implemented using python. (shrink)

Background: Dengue Disease is a mosquito-borne tropical disease caused by the dengue virus, symptoms typically begin three to fourteen days after infection. This may include a high fever, headache, vomiting, muscle and joint pains, and a characteristic skin rash. Dengue serology is applied in different settings, such as for surveillance, in health care facilities in endemic areas and in travel clinics in non-endemic areas. The applicability and quality of serological tests in dengue endemic regions has to be judged against (...) a background of potential cross reactivity with other flavi-viruses, difficulties in distinguishing primary from secondary infections and technological problems related to the fact that most dengue endemic regions are relatively poor of resources .Objectives: to help doctors and patients in diagnosing Dengue Disease and give them the information of how to prevent Dengue Disease and to be able to understand the signs and symptoms of Dengue Disease. Methods: We collected all relevant material for Dengue Disease. Then we designed and implemented a knowledge based system for diagnosing Dengue Disease using SL5 Object Language. Results: The knowledge based system was evaluated by a group of Patients and specialized doctors and they found it very friendly and easy to use. (shrink)

With the publication of A Theory of Justice in 1971, John Rawls not only rejuvenated contemporary political philosophy but also defended a Kantian form of Enlightenment liberalism called “justice as fairness.” Enlightenment liberalism stresses the development and exercise of our capacity for autonomy, while Reformation liberalism emphasizes diversity and the toleration that encourages it. These two strands of liberalism are often mutually supporting, but they conflict in a surprising number of cases, whether over the accommodation of group difference, the design (...) of civic education, or the promotion of liberal values internationally. During the 1980’s, however, Rawls began to jettison key Kantian characteristics of his theory, a process culminating in the 1993 release of Political Liberalism and completing the transformation of justice as fairness into a Reformation liberalism. -/- Reconstructing Rawls argues that this transformation was a tragic mistake because it jeopardized the most important features of his theory, viz. the lexical priorities of right, liberty, and fair equality of opportunity as well as the difference principle. Controversially, this book contends that Rawls’s so-called “political turn,” motivated by a newfound interest in diversity and the accommodation of difference, has been unhealthy for autonomy-based liberalism and has pushed liberalism more broadly towards cultural relativism, be it in the guise of liberal multiculturalism or critiques of cosmopolitan distributive-justice theories. The book then demonstrates that the central elements of justice as fairness can only be defended within the context of a Kantian Enlightenment liberalism and that Rawls’s hope for a more pluralistic grounding for his theory, endorsed by a wide variety of belief systems present in modern democratic societies, is illusory. -/- Reconstructing Rawls is the first book to systematically compare Rawls’s and Kant’s theories and the first to offer an internal critique and reconstruction of justice as fairness, reconceiving it as a comprehensive, universalistic Kantian liberalism. By doing so, it gives us both the vision of a liberal world order—“a republicanism of all states, together and separately,” as Kant put it—and a mode of justification addressed to all men and women, not as members of particular nations, races, and faiths, but as human beings, as citizens of the world. In short, it reclaims Rawls for the Enlightenment. (shrink)

Background: Depression (major depressive disorder) is a common and serious medical illness that negatively affects how you feel, the way you think and how you act. Fortunately, it is also treatable. Depression causes feelings of sadness and/or a loss of interest in activities once enjoyed. It can lead to a variety of emotional and physical problems and can decrease a person’s ability to function at work and at home. Depression affects an estimated one in 15 adults (6.7%) in any given (...) year. And one in six people (16.6%) will experience depression at some time in their life. Depression can strike at any time, but on average, first appears during the late teens to mid-20s. Women are more likely than men to experience depression. Some studies show that one-third of women will experience a major depressive episode in their lifetime. Objectives: The main goal of this expert system is to get the appropriate diagnosis of disease and the correct treatment and give the appropriate method of treatment through several tips that concern the disease and how to treat it and we will see it through the application on the expert system. Methods: in this paper the design of the proposed Expert System which was produced to help Psychologist in diagnosing depression disease through its symptoms such as: a loss of energy, a change in appetite, sleeping more or less, anxiety, reduced concentration, indecisiveness, restlessness, feelings of worthlessness, guilt or hopelessness and thoughts of self-harm or suicide. The proposed expert system presents an overview about depression disease is given, the cause of diseases is outlined and the treatment of disease whenever possible is given out. SL5 Object Expert System language was used for designing and implementing the proposed expert system. Results: The proposed depression disease diagnosis expert system was evaluated by psychologist students and they were satisfied with its performance. Conclusions: The Proposed expert system is very useful for psychologist, patients with depression and newly graduated psychologist. (shrink)

Abstract: In this research, an Artificial Neural Network (ANN) model was developed and tested to predict effect of oxygen consumption of thylakoid membranes (chloroplasts) from spinach after inhibition. A number of factors were identified that may affect of oxygen consumption of thylakoid membranes from spinach. Factors such as curve, herbicide, dose, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed and trained using the data from some inhibition of photosynthesis in (...) farms. The evaluation of testing the dataset shows that the ANN model is capable of correctly predicting the effect of oxygen consumption of thylakoid membranes (chloroplasts) from spinach after inhibition with 100% accuracy. (shrink)

The Knowledge Based System for Diagnosing Breast Cancer is used to assist medical students to improve their education on diagnosis and counseling the process of analyzing the biopsy image of the microscope, determining the type of tumor and the treatment method for each case and identifying the disease related questions. According to the Ministry of Health in its annual report in Gaza, between 2009 and 2014 there are 7069 cases of breast cancer, and in 2014 there are 1502 (...) cases of breast cancer . We are now in the age of visual information where 65% of the populations are visual learners; the Knowledge Based System is the easiest way to ensure students remember the information in the long term, using visual and textual information. The Knowledge Based System has an easy-to-use interface to help students diagnose the disease and enhance their information about the disease; the system can be used on smart phones. This Knowledge Based System can be divided into three main parts: The first part is specific questions for the patient, to help student to know questions type. The second part is the process of analyzing the biopsy sample image, to see if the biopsy image is intact or infected. The last part is the video shows ways to treat breast cancer in the form of animation. (shrink)

A ‘should’ question normally signals work for an ethicist but this ethicist’s task is complicated by the normative dimension of all the chapters in this volume. Each author was asked to come up with three recommendations from their own subject area – ’should’ statements deriving from the ‘is’ analysis that they present. If those prescriptions cover the relevant topics, what more is there for an ethicist to do? I have had a personal interest in obstetricians’ relationship with ‘older women’ since (...) being classified as an ‘elderly primigravida’ at the superannuated age of 26 years. Apart from that, however, what original contribution can I make? The convenors of the 56th RCOG Study Group gave me plentiful suggestions – perhaps a little too plentiful: How should the RCOG approach its constituencies, medical ethics, regulation and its relationship to government and the rest of the medical profession, i.e. the NHS and the market, vested interests, individuals or consumers, families, the unborn, doctors, drug companies, surrogacy, the unborn, trafficking, global adoption, law, research? I have to admit this was just too much for me. Instead, I want to argue for what may seem a self-evidently simple point. The RCOG describes its mission as ‘setting standards to improve women’s health’ – presumably all women. In the 6 years that I have served on the RCOG Ethics Committee, however, we have almost always been concerned with that minority of the female population who are of reproductive age. (shrink)

Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with (...) a heart disease or not using the medical features of the person. We used many different algorithms of machine learning such as Gaussian Mixture, Nearest Centroid, MultinomialNB, Logistic RegressionCV, Linear SVC, Linear Discriminant Analysis, SGD Classifier, Extra Tree Classifier, Calibrated ClassifierCV, Quadratic Discriminant Analysis, GaussianNB, Random Forest Classifier, ComplementNB, MLP Classifier, BernoulliNB, Bagging Classifier, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Gradient Boosting Classifier, Decision Tree Classifier, and Deep Learning to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of heart diseases in any person. The strength of the proposed model was very satisfying and was able to predict evidence of having a heart disease in a particular person by using Deep Learning and Random Forest Classifier which showed a good accuracy in comparison to the other used classifiers. The proposed heart disease prediction model will enhances medical care and reduces the cost. This study gives us significant knowledge that can help us predict the person with heart disease. The dataset was collected from Kaggle depository and the model is implemented using python. (shrink)

Abstract: The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems, necessitating accurate and timely forecasting of cases for effective mitigation strategies. In this research paper, we present a novel approach to predict COVID-19 cases using Artificial Neural Networks (ANNs), harnessing the power of machine learning for epidemiological forecasting. Our ANNs-based forecasting model has demonstrated remarkable efficacy, achieving an impressive accuracy rate of 97.87%. This achievement underscores the potential of ANNs in providing precise and data-driven insights into the (...) dynamics of the pandemic. However, this paper underscores the critical importance of a comprehensive evaluation beyond accuracy, including metrics such as sensitivity, specificity, and the area under the ROC curve (AUC-ROC), to assess the model's performance robustness. The research paper offers detailed insights into the architecture of the ANN model, encompassing critical hyperparameters, data preprocessing techniques, and regularization strategies employed to optimize model accuracy. Ethical considerations surrounding data privacy and potential biases within the COVID-19 dataset are also addressed. While the achieved accuracy is a significant milestone, this study underscores the dynamic and evolving nature of the pandemic, necessitating continuous model refinement and validation. Furthermore, it emphasizes the importance of considering false positives and false negatives in the context of public health decision-making. In conclusion, this research contributes to the arsenal of tools available for pandemic management by showcasing the potential of ANNs in COVID-19 case forecasting. It encourages ongoing exploration and adaptation of predictive models to enhance their applicability in real-world public health scenarios, ultimately contributing to more effective pandemic control and response efforts. (shrink)

Abstract: Potatoes are edible tubers, available worldwide and all year long. They are relatively cheap to grow, rich in nutrients, and they can make a delicious treat. The humble potato has fallen in popularity in recent years, due to the interest in low-carb foods. However, the fiber, vitamins, minerals, and phytochemicals it provides can help ward off disease and benefit human health. They are an important staple food in many countries around the world. There are an estimated 200 varieties (...) of potatoes, which can be classified into a number of categories based on the cooked texture and ingredient functionality. Using a public dataset of 2400 images of potatoes, we trained a deep convolutional neural network to identify 4 types (Red, Red Washed, Sweet, and White).The trained model achieved an accuracy of 99.5% of test set, demonstrating the feasibility of this approach. (shrink)

This study contributes to the micro-credit literature by addressing the lack of philosophical dialogue concerning the issue of trust between micro-credit NGOs and rural poor women. The study demonstrates that one of the root causes of NGOs’ contested roles in Bangladesh is the norm that they use (i.e., trust) to rationalize their micro-credit activities. I argue that Bangladeshi micro-credit NGOs’ trust in poor village women is not genuine because they resort to group responsibility sustained through aggressive surveillance. I maintain so (...) by drawing on a trust-based theoretical framework that uses various philosophical insights. Drawing on the same conceptual framework, I also contend, somewhat softening the previous claim, that if micro-credit trust is trust at all, it is at most strategic, not generalized. For being strategic, it has many undermining effects on local social solidarity norms, rendering Bangladeshi micro-credit NGOs and strategic trust an odd couple with no moral compass. To bring forth the moral impetus in micro-credit activities, I lay out some recommendations intended for organizations, managers, and policymakers, consistent with normative corporate social responsibility initiatives. However, further studies can be initiated based on this paper, suggesting its importance for future research. (shrink)

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines or software that work and reacts like humans, some of the computer activities with artificial intelligence are designed to include speech, recognition, learning, planning and problem solving. Deep learning is a collection of algorithms used in machine learning, it is part of a broad family of methods used for machine learning that are based on learning representations of data. Deep learning is used as (...) a technique to produce brain tumor detection and classification models using Magnetic Resonance Imaging (MRI) imaging for rapid and easy detection and identification of brain tumor. In this thesis, some ways and mechanisms will be reviewed to use deep learning techniques to produce a model for brain tumor detection. The goal is to find a good and effective way to detect brain tumor based on MRI to help the brain doctor in making decisions easily, accurately and rapidly. A recent report by the World Health Organization in February 2018 showed that the death rate from brain cancer or central nervous system (CNS) is the highest in the Asian continent. It is important to detect cancer early so that many of these lives can be saved. The model has been designed and implemented, including a dataset which consist of 10,000 images for brain tumor detection through the use of Deep learning algorithms based on neural networks. For testing, we have used our model, Inception, VGG16, MobileNet and ResNet models. The f-score accuracy we got for each model was as follows: Our model was 98.28, VGG16 was 99.86%, ResNet50 was 98.14%, MobileNet was 88,98%, and InceptionV3 was 99.88%. (shrink)

In this research, an Artificial Neural Network (ANN) model was developed and tested to predict Birth Weight. A number of factors were identified that may affect birth weight. Factors such as smoke, race, age, weight (lbs) at last menstrual period, hypertension, uterine irritability, number of physician visits in 1st trimester, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed and trained using the data from some birth cases in hospitals. The (...) evaluation of testing the dataset shows that the ANN model is capable of correctly predicting the birth weight with 100% accuracy. (shrink)

Automatic detection of diseases and anatomical landmarks in medical images by the use of computers is important and considered a challenging process that could help medical diagnosis and reduce the cost and time of investigational procedures and refine health care systems all over the world. Recently, gastrointestinal (GI) tract disease diagnosis through endoscopic image classification is an active research area in the biomedical field. Several GI tract disease classification methods based on image processing and machine learning techniques have (...) been proposed by diverse research groups in the recent past. However, yet effective and comprehensive deep ensemble neural network-based classification model with high accuracy classification results is not available in the literature. In this thesis, we review ways and mechanisms to use deep learning techniques to research on multi-disease computer-aided detection about gastrointestinal and identify these images. We re-trained five state-of-the-art neural network architectures, VGG16, ResNet, MobileNet, Inception-v3, and Xception on the Kvasir dataset to classify eight categories that include an anatomical landmark (pylorus, z-line, cecum), a diseased state (esophagitis, ulcerative colitis, polyps), or a medical procedure (dyed lifted polyps, dyed resection margins) in the Gastrointestinal Tract. Our models have showed results with a promising accuracy which is a remarkable performance with respect to the state-of-the-art approaches. The resulting accuracies achieved using VGG, ResNet, MobileNet, Inception-v3, and Xception were 98.3%, 92.3%, 97.6%, 90% and 98.2%, respectively. As it appears, the most accurate result has been achieved when retraining VGG16 and Xception neural networks with accuracy reache to 98% due to its high performance on training on ImageNet dataset and internal structure that support classification problems. (shrink)

Positivist medical historians, guided by the savoir of modern western biomedicine, have long depicted medieval medicine as an aberration along the continuum of scientific and medical progress. Historical epistemology, founded in the ideas of Cavailles, Foucault, Davidson, and Hacking, however, allows the historian to disrupt this false continuum and to unchain medieval medicine from modern medicine. Postmodernist approaches, such as those sourced in Lyotard, Barthes, and Derrida, allow the historian to further deconstruct medieval and modern medical discourse, revealing a multitude (...) of narrative lenses spinning around biomedical and biocultural strands. In liberating these two medical systems and setting them within the distinct historical and epistemological contexts that both shaped and were shaped by them, the historian can revision the theories, practices, and culture of medieval medicine without having to anachronistically justify them according to modern medical discourse. (shrink)

Chronic kidney disease (CKD) is a global health problem with high morbidity and mortality rate, and it induces other diseases. There are no obvious incidental effects during the starting periods of CKD, patients routinely disregard to see the sickness. Early disclosure of CKD enables patients to seek helpful treatment to improve the development of this disease. AI models can effectively assist clinical with achieving this objective on account of their fast and exact affirmation execution. In this appraisal, proposed a (...) Logistic relapse framework for diagnosing CKD. Proposed calculation like NAIVE BAYES , DECISION TREE , KSTAR , LOGISITIC , AND SVM and look at these calculation and get the most noteworthy precision .AI store, which has an enormous number of missing qualities. Missing characteristics are for the most part found, taking everything into account, clinical conditions since patients might miss a couple of assessments for various reasons. By separating the misjudgements delivered by the set up models and proposed a fused model that unites determined backslide and sporadic woods by using perceptron. (shrink)

This study employs a neural network to analyze the connection between smoking, drinking, and various health-related factors using a dataset of 5148 samples. Achieving an impressive 99.94% accuracy and an average training error of 0.0016, the model identifies influential factors such as serum aminotransferases, serum creatinine, sex, weight, and triglyceride levels. These findings enhance our understanding of lifestyle choices and their impact on health. This research underscores the potential of machine learning in studying complex health phenomena.

Vocal fry is a phonation, or voicing, in which an individual drops their voice below its natural register and consequently emits a low, growly, creaky tone of voice. Media outlets have widely acknowledged it as a generational vocal style characteristic of millennial women. Critics of vocal fry often claim that it is an exclusively female vocal pattern, and some say that the voicing is so distracting that they cannot understand what is being said under the phonation. Claiming that a phonation (...) is so distracting as to prevent uptake of the semantic content of an utterance associated with it is an extreme reaction, especially when accompanied by demands for women to change their phonation. We argue that this reaction limits women's communicative autonomy. We analyze the extreme reaction to female vocal fry, which we characterize as a non-content-based response, from the perspectives of philosophy of language, feminist epistemology, and linguistics. We argue that when fry is heard as annoying and distracting, it is because the hearer interprets the speaker as echoing an utterance from a position of authority to which she is not entitled. We show that this reaction encodes conscious or unconscious sexist attitudes toward women's voices. (shrink)

Medicine, like law, is a pragmatic, probabilistic activity. Both require that decisions be made on the basis of available evidence, within a limited time. In contrast to law, medicine, particularly evidence-based medicine as it is currently practiced, aspires to a scientific standard of proof, one that is more certain than the standards of proof courts apply in civil and criminal proceedings. But medicine, as Dr. William Osler put it, is an "art of probabilities," or at best, a (...) "science of uncertainty." One can better practice medicine by using other evidentiary standards in addition to the "scientific." To employ only the scientific standard of proof is inappropriate, if not impossible; furthermore, as this review will show, its application in medicine is fraught with bias. Evidence is information. It supports or undermines a proposition, whether a hypothesis in science, a diagnosis in medicine, or a fact or point in question in a legal investigation. In medicine, physicians marshal evidence to make decisions on how to best prevent, diagnose, and treat disease, and improve health. In law, courts decide the facts and render justice. Judges and juries assess evidence to establish liability, to settle custody and medical issues, and to determine a defendant's guilt or innocence. (shrink)

Breast cancer remains a significant health concern worldwide, necessitating the development of effective diagnostic tools. In this study, we employ a neural network-based approach to analyze the Wisconsin Breast Cancer dataset, sourced from Kaggle, comprising 570 samples and 30 features. Our proposed model features six layers (1 input, 1 hidden, 1 output), and through rigorous training and validation, we achieve a remarkable accuracy rate of 99.57% and an average error of 0.000170 as shown in the image below. Furthermore, (...) our investigation identifies the most influential features in breast cancer diagnosis, shedding light on the key determinants of malignancy. Notably, we find that factors such as fractal dimension_se, symmetry worst, compactness_worst, symmetry_se, and smoothness_se play pivotal roles in distinguishing between benign and malignant cases. This research contributes to the ongoing efforts to enhance breast cancer diagnosis, providing valuable insights into feature importance and showcasing the potential of neural networks in medical applications. Our findings have implications for improving early detection and treatment strategies, ultimately contributing to improved patient outcomes. (shrink)

In order to achieve the World Health Organization’s Millennium Development Goal of reducing maternal mortality by three quarters by 2015, a strong global commitment is needed to address this issue in low-income nations where the risk to women is greatest. A comprehensive international effort must include provision of obstetric and general medical care as well as community-based interventions, with an emphasis on the latter in nations where the majority of women deliver babies at home without a trained attendant. (...) Methods: This systematic analysis evaluates interventions published in Medline and CINAHL whose outcome measure is maternal mortality. Analysis includes components of the intervention, nation and maternal death rates. Results: Nine studies documented the effectiveness of various clinical and community-based interventions, including specific drug regimens and procedures, in reducing the risk of maternal death. Six studies reported interventions that did not significantly alter maternal mortality outcomes, and the intervention in one study demonstrated increased risk of maternal death. Conclusion: The dearth of evidence highlights the need for increased focus on translational research that bridges the gap between medical advances and community-based interventions that are feasible in low-income nations. This cannot be accomplished without a stronger commitment to reducing maternal mortality by global health practitioners and researchers. (shrink)

The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we (...) initiated the development of the community-based Coronavirus Infectious Disease Ontology in early 2020. -/- As an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment. -/- CIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications. (shrink)

Malaria is a life-threatening disease spread to humans by some types of mosquitoes. It is mostly found in tropical countries. It is preventable and curable. The infection is caused by a parasite and does not spread from person to person. Symptoms can be mild or life-threatening. Mild symptoms are fever, chills and headache. Severe symptoms include fatigue, confusion, seizures, and difficulty breathing. Infants, children under 5 years, pregnant women, travelers and people with HIV or AIDS are at higher risk of (...) severe infection. Discovering this disease is a good step for proper and correct treatment. Determining the treatment with high accuracy depends on the method used in the diagnosis. Correctly, expert systems can greatly help to avoid spreading this disease. The expert system diagnoses malaria disease correctly to facilitate doctors to find the correct treatment based on the appropriate diagnosis. Objectives: An expert system has been established based on CLIPS to diagnose malaria disease. (shrink)

Traditionally, intuitions about cases have been taken as strong evidence for a philosophical position. I argue that intuitions about concept deployment have epistemic value while intuitions about matters of fact have none. I argue this by use of the explanationist criterion which contends that S is justified in believing only those propositions which are part of the best explanation of S’s making the judgements she makes. This criterion accords with scientific practice. Bealer suggests, as a defence of intuition, that (...) naturalists rely on intuition in their arguments against intuition and thus that their arguments are self-undermining. But beliefs based on intuitions about the application of concepts, like ‘justification’ and ‘theory,’ are epistemically justified by the explanationist criterion. It is only intuitions about matters of fact which are rejected. Thus, epistemically-respectable intuitions about concepts are used to undermine intuitions about metaphysics. There is nothing self-undermining about this. (shrink)

Current COVID-19 pandemic and previous SARS/MERS outbreaks have caused a series of major crises to global public health. We must integrate the large and exponentially growing amount of heterogeneous coronavirus data to better understand coronaviruses and associated disease mechanisms, in the interest of developing effective and safe vaccines and drugs. Ontologies have emerged to play an important role in standard knowledge and data representation, integration, sharing, and analysis. We have initiated the development of the community-based Coronavirus Infectious Disease (...) Ontology (CIDO). As an Open Biomedical Ontology (OBO) library ontology, CIDO is an open source and interoperable with other existing OBO ontologies. In this article, the general architecture and the design patterns of the CIDO are introduced, CIDO representation of coronaviruses, phenotypes, anti-coronavirus drugs and medical devices (e.g. ventilators) are illustrated, and an application of CIDO implemented to identify repurposable drug candidates for effective and safe COVID-19 treatment is presented. (shrink)

Abstract: background: Dental and oral health is an essential part of your overall health and well-being. Poor oral hygiene can lead to dental cavities and gum disease, and has also been linked to heart disease, cancer, and diabetes. Maintaining healthy teeth and gums is a lifelong commitment. The earlier you learn proper oral hygiene habits — such as brushing, flossing, and limiting your sugar intake — the easier it’ll be to avoid costly dental procedures and long-term health (...) issues. (Healthline, n.d.) Objectives The main goal of this expert system is to get the appropriate diagnosis of disease and the correct treatment by presenting suggestions on Tooth Problems to the user by asking about symptoms. (shrink)

Patient centred diagnosis is best practised through shared decision making; an iterative dialogue between doctor and patient, whichrespects a patient’s needs, values, preferences, and circumstances. -/- Shared decision making for diagnostic situations differs fundamentally from that for treatment decisions. This has important implications when considering its practical application. -/- The nature of dialogue should be tailored to the specific diagnostic decision; scenarios with higher stakes or uncertainty usually require more detailed conversations.

Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. The aim of this study is to develop a deep learning model to predict the gender from retinal fundus images. The proposed model was based on the Xception pre-trained model. The proposed model was trained on 20,000 retinal fundus images from Kaggle depository. The dataset was preprocessed them split into three datasets (training, validation, Testing). After training and cross-validating the (...) proposed model, it was evaluated using the testing dataset. The result of testing, the area under receiver operating characteristic curve (AUROC) of the model was 0.99, precision, recall, f1-score and accuracy were 99%, precision, recall, f1-score and accuracy were 96.83%, 96.83%, 96.82% and 96.83% respectively.. Clinicians are presently unaware of dissimilar retinal feature variants between females and males, stressing the importance of model explain ability for the prediction of gender from retinal fundus images. The proposed deep learning may enable clinician-driven automated discovery of novel visions and disease biomarkers. (shrink)

Chest pain is the pain felt in the chest by infants, children and adolescents. In most cases the pain is not associated with the heart. It is mainly recognized by the observance or report of pain by the infant, child or adolescent by reports of distress by parents or care givers. Chest pain is not unusual in children. Lots of children are seen in ambulatory clinics, emergency rooms and hospitals and cardiology clinics. Usually there is a benign cause for the (...) pain for utmost children. Certain patients have conditions that are serious and perhaps life-threatening. Chest pain in pediatric patients needs careful physical examination and a detailed history that would point to the possibility of a serious cause. Researches of pediatric chest pain are scarce. It has been difficult to create evidence-based guidelines for evaluation. In this paper we propose an expert system to help doctors and parents, and care giver in diagnosing chest pain in infants and children. This expert System is design and implemented in SL5 Object language. (shrink)

Accurate assessment and comprehension of smoking behavior are pivotal for elucidating associated health risks and formulating effective public health strategies. In this study, we introduce an innovative approach to predict and analyze smoking prevalence using an artificial neural network (ANN) model. Leveraging a comprehensive dataset spanning multiple years and geographic regions, our model incorporates various features, including demographic data, economic indicators, and tobacco control policies. This research investigates smoking trends with a specific focus on gender-based analyses. These (...) findings are pivotal for enhancing public health initiatives aimed at mitigating tobacco use, assessing associated health risks, and mitigating smoking-related diseases. (shrink)

A newborn baby is an infant within the first 28 days of birth. Diagnosis and treatment of infant diseases require specialized medical resources and expert knowledge. However, there is a shortage of such professionals globally, particularly in low-income countries. To address this challenge, a knowledge-based system was designed to aid in the diagnosis and treatment of neonatal diseases. The system utilizes both machine learning and health expert knowledge, and a hybrid data mining process model was used to extract (...) knowledge from a clinical dataset. The PART algorithm achieved the highest performance result with 98.06% accuracy under 10-fold cross-validation, and the generated rules were used to develop the knowledge-based system. The system achieved 90.9% accuracy in system performance testing and 89.2% in user acceptance testing, and is intended to serve as an assistant tool for healthcare experts. (shrink)