Chronic Diseases

Objective: Hashimoto’s thyroiditis is the most common autoimmune thyroid disease. Papillary thyroid cancer is the most common thyroid cancer. Whether Hashimoto’s thyroiditis is a predisposing factor for papillary thyroid cancer remains unclear. In this study, the frequency of papillary thyroid cancer was investigated in patients with Hashimoto’s thyroiditis who underwent total thyroidectomy. -/- Methods: In this study, 534 patients were screened retrospectively. Preoperative thyroid function tests, anti-thyroid antibodies, ultrasonography findings, fine-needle aspiration biopsies, and thyroidectomy pathology results were examined. According to (...) the pathology, 139 patients had Hashimoto’s thyroiditis. Patients with Hashimoto’s thyroiditis (group 1) and non-Hashimoto’s thyroiditis (group 2) were compared. -/- Results: Papillary thyroid cancer was found in 70 patients (50.4%) in group 1 and 156 patients (39.5%) in group 2 (P=.026). The odds ratio was 1.59 (95% CI; 1.07-2.34). There was no difference for sex, age, tumor size, microcarcinoma–macrocarcinoma distribution, subtype, number of tumor focus, lymph node metastasis, vessel invasion, and extrathyroidal spread. -/- Conclusion: We found more papillary thyroid cancer and less capsular invasion in patients with Hashimoto’s thyroiditis, but we did not find any differences between groups in terms of age, gender, tumor size, vascular invasion, and metastasis. According to these findings, Hashimoto’s thyroiditis may be a risk factor for papillary thyroid cancer. (shrink)

Since its initial publication in 2018, Professor Anya Plutynski’s Explaining Cancer: Finding Order in Disorder has garnered a great deal of accolades.1 In 2021, The London School of Economics and Political Science conferred Professor Plutynski the Lakatos Award, recognizing the book’s significant contribution to the philosophy of science. On the heels of its recent reissuing as a paperback, it is an ideal time to revisit this remarkable work.

Lung cancer is a serious and deadly disease that affects the lungs, which are responsible for taking in oxygen and expelling carbon dioxide from the body. The disease can develop in any part of the lungs and is usually caused by smoking or exposure to certain chemicals. The main Objective: of this expert system is to provide an accurate diagnosis of lung cancer and the appropriate treatment options. In this paper, Methods: we present the design and implementation of an expert (...) system that can assist physicians in diagnosing lung cancer and determining the best course of treatment. The system provides an overview of the disease, its causes, symptoms, diagnosis, and treatment, along with relevant medical information. Results: The system was developed using the Clips expert system language and evaluated by medical professionals who found it to be an effective tool for diagnosing and treating lung cancer. Conclusion: The proposed expert system has the potential to improve patient outcomes and reduce healthcare costs associated with the treatment of lung cancer. (shrink)

Cancer biologists ascribe normal functions to parts of cancer. Normal functions are activities that parts of systems are in some minimal sense supposed to perform. Cancer biologists’ finding normality within the abnormality of cancer pose difficulties for two main approaches to normal function. One approach claims that normal functions are activities that parts are selected for. However, some parts of cancers that have normal functions aren’t selected to perform them. The other approach claims that normal functions are part-activities typical for (...) the system and that contribute to survival/reproduction. However, cancers are too heterogeneous to establish what’s typical across a type. (shrink)

Cancer biology features the ascription of normal functions to parts of cancers. At least some ascriptions of function in cancer biology track local normality of parts within the global abnormality of the aberration to which those parts belong. That is, cancer biologists identify as functions activities that, in some sense, parts of cancers are supposed to perform, despite cancers themselves having no purpose. The present paper provides a theory to accommodate these normal function ascriptions—I call it the Modeling Account of (...) Normal Function (MA). MA comprises two claims. First, normal functions are activities whose performance by the function-bearing part contributes to the self-maintenance of the whole system and, thereby, results in the continued presence of that part. Second, MA holds that models of system-level activities that are (partly) constitutive of self-maintenance are improved by including a representation of the relevant function-bearing part and by making reference to the activity/activities that part performs which contribute(s) to those system-level activities. I contrast MA with two other accounts that seek to explicate the ascription of normal functions in biology, namely, the organizational account and the selected effects account. Both struggle to extend to cancer biology. However, I offer ecumenical readings which allow them to recover some ascriptions of normal function to parts of cancers. So, though I contend that MA excels in this respect, the purpose of this paper is served if it provides materials for bridging the gap between cancer biology, philosophy of cancer, and the literature on function. (shrink)

There is a lot of conceptual engineering going on in medical research. I substantiate this claim with two examples, the medical debate about cancer classification and about obesity as a disease I also argue that the proper target of conceptual engineering in medical research are experts’ conceptions. These are explicitly written down in documents and guidelines, and they bear on research and policies. In the second part of the chapter, I propose an externalist framework in which conceptions have both the (...) explanatory power of psychological concepts and that of semantic concepts. It is likely, however, that human activities and practices distinct from medical research, and regulated by different practices and epistemic rules, call for different targets for conceptual engineering. I conclude with indicating an open agenda of problems for philosophers of medicine interested in conceptual engineering. (shrink)

In this paper, we developed an Artificial Neural Network (ANN) for detect the absence or presence of lung cancer in human body. Symptoms were used to diagnose the lung cancer, these symptoms such as Yellow fingers, Anxiety, Chronic Disease, Fatigue, Allergy, Wheezing, Coughing, Shortness of Breath, Swallowing Difficulty and Chest pain. They were used and other information about the person as input variables for our ANN. Our ANN established, trained, and validated using data set, which its title is “survey lung (...) cancer”. Model evaluation showed that the ANN model is able to detect the absence or presence of lung cancer with 96.67 % accuracy. -/- . (shrink)

Background: Chronic myeloid leukemia (CML) is a hematopoietic stem cell disease, associated with a reciprocal translocation between chromosomes 9 and chromosome 22, lead to the formation of the BCRABL fusion gene (Philadelphia chromosome). This fusion gene is believed to play golden role in the initial development of CML with constitutive tyrosine kinase activation. Successful use of tyrosine kinase inhibiters (TKIs) play a role in improve survival and increase prevalence of CML, but un fortunately mutations in the BCR-ABL kinase domain may (...) cause, or contribute to increase, resistance to TKIs in CML patients. . Objective: This study was designed to assess the association of five most common BCR-ABL kinase domain mutations (T315I, M351T, E255K, M244V and E255V) with resistance state of CML patients on TKIs in Iraqi Middle Euphrates region. Patients and methods: A retrospective case-control study in which 85 patients with chronic myeloid leukemia in chronic phase (45 patients as cases group and 40 patient as control group) were selected from three hemato-oncology centers in middle Euphrates in Iraq during the period from January 2016 till October 2016 out of a total of 240 CML patients (108 male and 132 female) who were registered during this period in these three centers and all patients on TKI (Imatinib and Nilotinib). Venous blood sampling done for BCRABL kinase domain mutations screening. Results: four patients from cases group (4/45) were carriers of one of five selected ABL kinase domain mutations and no one of control group. T315I mutation was detected in 3/45 (6.6 %) of resistant patients, with a significant risk association to develop resistance to TKI therapy (odd ratio and C. I.) (6.67, 0.3340 - 133.2255). E255V was detected in 1/45 (2.2 %) and also had significant risk association to develop resistance to TKIs (odd ratio, C.I.) (2.73, 0.1081 -68.9424). No one of these mutations had significance correlation with demographic or hematological features. M351T, E255K and M244V were not detected in any one of our study groups CML patients. Conclusions: T315I and E255V among five ABL kinas domain mutations were detected in our CML patients with resistance to TKIs. All of them may play a role in development variable degree of resistance to first and second generation TKIs weather primary or secondary.T315I mutation is most common mutation within BCR-ABL domain kinase gene. (shrink)

In this paper, we developed an Artificial Neural Network (ANN) for detect the absence or presence of lung cancer in human body. Symptoms were used to diagnose the lung cancer, these symptoms such as Yellow fingers, Anxiety, Chronic Disease, Fatigue, Allergy, Wheezing, Coughing, Shortness of Breath, Swallowing Difficulty and Chest pain. They were used and other information about the person as input variables for our ANN. Our ANN established, trained, and validated using data set, which its title is “survey lung (...) cancer”. Model evaluation showed that the ANN model is able to detect the absence or presence of lung cancer with 96.67 % accuracy. (shrink)

This chapter considers ethical issues involved in genetic testing and screening for susceptibility to various forms of cancer.

We raise a problem of applicability of RCTs to validate nuclear diagnostic imaging tests. In spite of the wide application of PET and other similar techniques that use radiopharmaceuticals for diagnostic purposes, RCT-based evidence on their validity is sparse. We claim that this is due to a general conceptual problem that we call Prevalence of Treatment, which arises in connection with designing RCTs for testing any diagnostic procedure in the present context of medical research, and is particularly apparent in this (...) case. We also identify three practical reasons why RCTs do not qualify as the best option for PET validation, which have to do with specific characteristics of nuclear diagnostic imaging, and of radiopharmaceuticals. The paper is meant to contribute both to the philosophical discussion on the EBM hierarchy of evidence, and on the specific debate on radiopharmaceuticals in nuclear medicine. (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)

The “Expert 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 expert system is the easiest way to ensure students remember the information in the long term, using visual and textual information. The expert 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 expert 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)

Transoral laser microsurgery applies to the piecemeal removal of malignant tumours of the upper aerodigestive tract using the CO2 laser under the operating microscope. This method of surgery is being increasingly popularised as a single modality treatment of choice in early laryngeal cancers (T1 and T2) and occasionally in the more advanced forms of the disease (T3 and T4), predomi- nantly within the supraglottis. Thomas Kuhn, the American physicist turned philosopher and historian of science, coined the phrase ‘paradigm shift’ in (...) his groundbreaking book The Structure of Scientific Revolutions. He argued that the arrival of the new and often incompatible idea forms the core of a new paradigm, the birth of an entirely new way of thinking. This article discusses whether Steiner and col- leagues truly brought about a paradigm shift in oncological surgery. By rejecting the principle of en block resection and by replacing it with the belief that not only is it oncologically safe to cut through the substance of the tumour but in doing so one can actually achieve better results, Steiner was able to truly revolutionise the man- agement of laryngeal cancer. Even though within this article the repercussions of his insight are limited to the upper aerodigestive tract oncological surgery, his willingness to question other peoples’ dogma makes his contribution truly a genuine paradigm shift. (shrink)

In this paper we present applications of the ACGT Master Ontology (MO) which is a new terminology resource for a transnational network providing data exchange in oncology, emphasizing the integration of both clinical and molecular data. The development of a new ontology was necessary due to problems with existing biomedical ontologies in oncology. The ACGT MO is a test case for the application of best practices in ontology development. This paper provides an overview of the application of the ontology within (...) the ACGT project thus far. (shrink)

Ontological principles are needed in order to bridge the gap between medical and biological information in a robust and computable fashion. This is essential in order to draw inferences across the levels of granularity which span medicine and biology, an example of which include the understanding of the roles of tumor markers in the development and progress of carcinoma. Such information integration is also important for the integration of genomics information with the information contained in the electronic patient records in (...) such a way that real time conclusions can be drawn. In this paper we describe a large multi-granular datasource built by using ontological principles and focusing on the case of colon carcinoma. (shrink)

I argue that ductal carcinoma in situ (DCIS), the tumor most commonly diagnosed by breast mammography, cannot be confidently classified as cancer, that is, as pathological. This is because there may not be dysfunction present in DCIS—as I argue based on its high prevalence and the small amount of risk it conveys—and thus DCIS may not count as a disease by dysfunction-requiring approaches, such as Boorse’s biostatistical theory and Wakefield’s harmful dysfunction account. Patients should decide about treatment for DCIS based (...) on the risks it poses and the risks and benefits of treatment, not on its disease status. (shrink)

In accordance with the critical women’s health literature recounting the ways that women are encouraged to submit themselves to various sorts of health “imperatives”, I investigate the messages tacitly conveyed to women in “campaigns for the cure” and breast cancer awareness efforts, which, I argue, overemphasizes a “positive attitude”, healthy lifestyle, and cure rather than prevention of this life-threatening disease. I challenge that the message of hope pervading breast cancer discourse silences the despair felt by many women, furthers a tacit (...) blaming for disease infliction via a rhetoric of personal responsibility, underemphasizes other cogent health determinants like environmental toxicity, and undermines legitimate critiques of current biomedical practices like widespread mammography. While finding a cure for breast cancer is a laudable and worthwhile healthcare goal that can understandably be shared by women’s health activists, corporate sponsors, and the medical community, this paper resists the current formation of campaigns for the cure and “pink ribbon activism” in general. (shrink)

As a response to the opioid epidemic in the United States, the Centers for Disease Control and Prevention (CDC) published the CDC Guideline for Prescribing Opioids for Chronic Pain in 2016. This document served as a means to reduce risks and address harms of opioid use by recommending that clinicians conduct periodic urine drug testing for patients on chronic opioid therapy. As an unintended result of this recommendation, providers began using unexpected urine drug test results as a reason to dismiss (...) patients from practice, both out of concern for their patients’ wellbeing as well as their own legal risks. Using Husserl’s and Heidegger’s phenomenology, we argue that this science-based, black-and-white practice does not heed the patient as a whole person. Instead, we recommend a more contextual, patient-centered approach that can help us to better understand and manage patient needs in such contexts. (shrink)

Both patients and clinicians frequently report problems around communicating and assessing pain. Patients express dissatisfaction with their doctors and doctors often find exchanges with chronic pain patients difficult and frustrating. This chapter thus asks how we could improve pain communication and thereby enhance outcomes for chronic pain patients. We argue that improving matters will require a better appreciation of the complex meaning of pain terms and of the variability and flexibility in how individuals think about pain. We start by examining (...) the various accounts of the meaning of pain terms that have been suggested within philosophy and suggest that, while each of the accounts captures something important about our use of pain terms, none is completely satisfactory. We propose that pain terms should be viewed as communicating complex meanings, which may change across different communicative contexts, and this in turn suggests that we should view our ordinary thought about pain as similarly complex. We then sketch what a view taking seriously this variability in meaning and thought might look like, which we call the “polyeidic” view. According to this view, individuals tacitly occupy divergent stances across a range of different dimensions of pain, with one agent, for instance, thinking of pain in a much more “bodycentric” kind of way, while another thinks of pain in a much more "mindcentric” way. The polyeidic view attempts to expand the multidimensionality recognised in, e.g., biopsychosocial models in two directions: first, it holds that the standard triumvirate— dividing sensory/cognitive/affective factors— needs to be enriched in order to capture important distinctions within the social and psychological dimensions. Second, the polyeidic view attempts to explain (at least in part) why modulation of experience by these social and psychological factors is possible in the first place. It does so by arguing that because the folk concept of pain is complex, different weightings of the different parts of the concept can modulate pain experience in a variety of ways. Finally, we argue that adopting a polyeidic approach to the meaning of pain would have a range of measurable clinical outcomes. (shrink)

Introduction Current treatments for pain have limited benefits and worrying side effects. Some studies suggest that pain is reduced when clinicians deliver positive messages. However, the effects of positive messages are heterogeneous and have not been subject to meta-analysis. We aimed to estimate the efficacy of positive messages for pain reduction. -/- Methods We included randomized trials of the effects of positive messages in a subset of the studies included in a recent systematic review of context factors for treating pain. (...) Several electronic databases were searched. Reference lists of relevant studies were also searched. Two authors independently undertook study selection, data extraction, risk of bias assessment, and analyses. Our primary outcome measures were differences in patient- or observer-reported pain between groups who were given positive messages and those who were not. -/- Results Of the 16 randomized trials (1703 patients) that met the inclusion criteria, 12 trials had sufficient data for meta-analysis. The pooled standardized effect size was −0.31 (95% confidence interval [CI] −0.61 to −0.01, p = 0.04, I2 = 82%). The effect size remained positive but not statistically significant after we excluded studies considered to have a high risk of bias (standard effect size −0.17, 95% CI −0.54 to 0.19, P = 0.36, I2 = 84%). -/- Conclusion Care of patients with chronic or acute pain may be enhanced when clinicians deliver positive messages about possible clinical outcomes. However, we have identified several limitations of the present study that suggest caution when interpreting the results. We recommend further high-quality studies to confirm (or falsify) our result. -/- FUNDING -/- Alexander Mebius research has been funded through the ERC grant "Philosophy of Pharmacology: Safety, Statistical Standards, and Evidence Amalgamation" (GA: 639276). (shrink)

Introduction: The incidence of chronic low back pain (LBP) is very high in Bangladesh. There is a high prevalence of psychiatric diseases among chronic low back pain patients. But primary care physicians and specialists do not screen this association. The aims of this study were to evaluate the incidence and pattern of psychiatric diseases in chronic low back pain patients. Materials and methods: A prospective cross-sectional hospital-based study of 135 chronic low back pain patients using simple, direct, standardized questionnaire including (...) history and detailed psychiatric evaluation by a consultant psychiatrist from January to December 2014. Results: A total 69 chronic low back pain patients (51.1%) were diagnosed with psychiatric problems. Predominant group were female (65.9%), above 40 years of age (60.7%). Depression (44.9 %) and anxiety (31.9%) were the major psychiatric illness diagnosed (76.8%). Only 17.4 % patients expects complete cure. Fear of disease outcome was the commonest precipitating factor found (66.6%). With the adjuvant psychiatric management, 81.1% showed improvement clinically. Conclusion: Detecting the level of psychiatric illness among chronic low back pain patients at early stage will improve the treatment outcome. A referral system to psychiatrists by physicians needs to develop to screen the mental disorder symptoms to treat the problem. (shrink)

The paper is written in response to those who fail to recognize the relation between a patient's mental competency and her state of pain. Some clinicians claim that a proper diagnosis can only be made in the absent of analgesia. Rather, the patient's state of pain directly affects her mental competency and thus her ability to give valid consent. Clinicians should rethink their approach to diagnosis when the patient is in pain.

The diabetic foot is a global threat to public health because it can result in infection and amputation, as well as cause the patient to experience considerable pain and incur financial costs. The condition of patients with diabetic foot in North China is distinguished by more severe local ulcers, a worse prognosis, and a longer duration of disease than that of patients with diabetic foot in the south. Through appropriate preventive measures, the diabetic foot can be effectively avoided. This study (...) assesses the existing knowledge, attitudes and practices associated with diabetic foot prevention among adults with diabetes living in rural areas of North China. (shrink)

Objectives: This study aimed to fill the gap between Vietnamese diabetic patients' needs and care through a qualitative study asking about their experiences with diabetes and quality of care. -/- Methods: Interviews with five diabetic patients were conducted at a tertiary general hospital located in southern Vietnam. The transcribed data were first subjected to quantitative text analysis using KH Coder to identify major categories of frequently used words, followed by a qualitative analysis of selected cases using the Steps for Coding (...) and Theorization (SCAT) method. -/- Results: The major categories of frequently used words were chronic health conditions, services, facilities, insurance, patient-doctor communication, and medication. SCAT analysis of three selected cases identified six themes: “Disregarding the disease at the early stage,” “Fear of complications,” “Satisfaction with hospital services and medical staff,” “Insurance-related problems,” “Long waiting times,” and “Communication barriers between patients and doctors.” Patients were satisfied with improved hospital facilities and services; however, the overloading of one hospital led to long waiting times and communication difficulties with doctors. Difficulties with health insurance were also observed, and patients were rather passive in disease management and needed to be empowered through improved communication with doctors and other care providers. -/- Conclusion: These findings from our trial of introducing a qualitative study into service evaluation suggest that listening to patients can help health providers learn their perspectives and be more responsive to their needs. (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)

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)

In today’s world it became difficult for daily routine check-up. The Heart disease system is an end user support and online consultation project. Here the motto behind it is to make a person to know about their heart related problem and according to it formulate them how much vital the disease is. It will be easy to access and keep track of their respective health. Thus, it’s important to predict the disease as earliest. Attributes such as Bp, Cholesterol, Diabetes are (...) fed into Classification methods of Machine Learning are been used to predict risk of heart disease. (shrink)

Coronary Heart Disease (CHD) is one of the leading causes of death nowadays. Prediction of the disease at an early stage is crucial for many health care providers to protect their patients and save lives and costly hospitalization resources. The use of machine learning in the prediction of serious disease events using routine medical records has been successful in recent years. In this paper, a comparative analysis of different machine learning techniques that can accurately predict the occurrence of CHD events (...) from clinical data was performed. Four machine learning classifiers, namely Logistic Regression, Support Vector Machine (SVM), K- Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) Neural Networks were identified and applied to a dataset of 462 medical instances and 9 features as well as the class feature from the South African Heart Disease data retrieved from the KEEL repository. The dataset consists of 302 records of healthy patients and 160 records of patients who suffer from CHD. In order to handle the imbalanced classification problem, the K-means algorithm along with Synthetic Minority Oversampling TEchnique (SMOTE) was used in this study. The empirical results of applying the four machine learning classifiers on the oversampled dataset have been very promising. The results reported using different evaluation metrics showed that SVM has achieved the highest overall prediction performance. (shrink)

The paper aims to examine the conceptual model on the impact of sociodemographic towards coronary heart disease. There is still limited study on the association between cost of living particularly the cost of food and housing and its impact on coronary heart disease. Therefore, this study incorporates five essential sociodemographic attributes namely, cost of food, children education cost, housing cost, smoking habit and gender. The study provides greater understanding on how the cost of living and lifestyle may give impact to (...) coronary heart disease. It may provide the awareness among the regulators as the society faces burden of high cost of living. The burden may threaten the Malaysians quality of life particularly the lower income group. (shrink)

Nostalgia as one of the complex emotions has been challenged over the past few decades due to its psychological and physiological functions. The present experiment investigates the effect of recalling nostalgic memories on amelioration of homeostatic and health state of people with cardiovascular disease. Method: The participants were 30 patients who were hospitalized for angiography procedure. The research was based on an experimental design with randomized and post-test groups. The instruments used included a thermometer with ° C, a checkout manipulation (...) test, a blood pressure monitor, an ice container and a manual timer, a pain intensity perception scale and Batcho’s nostalgia questionnaire. Results: The results showed that there was a significant difference between the two groups in perceived temperature, the resilience in the face of pain, and the perceived pain. The experimental group perceived higher temperature and showed greater resilience in the face of pain and also lower pain intensity was reported. There was no significant difference in blood pressure between the two groups. Conclusion: The results of this study provide significant evidence for the embodiment emotions hypothesis and positive role of Nostalgia in psychological and physiological homeostatic. Further practical implications of results and suggestions for future research were considered. (shrink)

Assumptions about obesity—e.g., its connection to ill health, its causes, etc.—are still prevalent today, and they make up what I call the medical model of fatness. In this paper, I argue that the medical model was established on the basis of insufficient evidence and has nevertheless continued to be relied upon to justify methodological choices that further entrench the assumptions of the medical model. These choices are illegitimate in so far as they conflict with both the epistemic and social aims (...) of obesity research. I conclude the paper with a partial solution to these epistemic and social shortcomings. (shrink)

There is a lot of conceptual engineering going on in medical research. I substantiate this claim with two examples, the medical debate about cancer classification and about obesity as a disease I also argue that the proper target of conceptual engineering in medical research are experts’ conceptions. These are explicitly written down in documents and guidelines, and they bear on research and policies. In the second part of the chapter, I propose an externalist framework in which conceptions have both the (...) explanatory power of psychological concepts and that of semantic concepts. It is likely, however, that human activities and practices distinct from medical research, and regulated by different practices and epistemic rules, call for different targets for conceptual engineering. I conclude with indicating an open agenda of problems for philosophers of medicine interested in conceptual engineering. (shrink)

Background: Arthritis is very common but is not well understood. Actually, “arthritis” is not a single disease; it is an informal way of referring to joint pain or joint disease. There are more than 100 different types of arthritis and related conditions. People of all ages, sexes and races can and do have arthritis, and it is the leading cause of disability in America. More than 50 million adults and 300,000 children have some type of arthritis. It is most common (...) among women and occurs more frequently as people get older. Common arthritis joint symptoms include swelling, pain, stiffness and decreased range of motion. Symptoms may come and go. They can be mild, moderate or severe. They may stay about the same for years, but may progress or get worse over time. Severe arthritis can result in chronic pain, inability to do daily activities and make it difficult to walk or climb stairs. Arthritis can cause permanent joint changes. These changes may be visible, such as knobby finger joints, but often the damage can only be seen on X-ray. Some types of arthritis also affect the heart, eyes, lungs, kidneys and skin as well as the joints. 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 Orthopedist in diagnosing Arthritis disease through its symptoms such as: pain on pressure in a joint , Inflammation indicated by joint swelling, Stiffness especially in the morning , Loss of flexibility of joint, Limited, joint movement, Deformity of the joints , Weight loss and fatigue , Non-specific fever and Crepitus. The proposed expert system presents an overview about Arthritis 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 Arthritis disease diagnosis expert system was evaluated by Orthopedics students and they were satisfied with its performance. Conclusions: The Proposed expert system is very useful for Orthopedist, patients with arthritis and newly graduated Orthopedics students. (shrink)

Claims about whether or not infertility is a disease are sometimes invoked to defend or criticize the provision of state-funded treatment for infertility. In this paper, I suggest that this strategy is problematic. By exploring infertility through key approaches to disease in the philosophy of medicine, I show that there are deep theoretical disagreements regarding what subtypes of infertility qualify as diseases. Given that infertility's disease status remains unclear, one cannot uncontroversially justify or undermine its claim to medical treatment by (...) claiming that it is or is not a disease. Instead of focusing on disease status, a preferable strategy to approach the debate about state-funded treatment is to explicitly address the specific ethical considerations raised by infertility. I show how this alternative strategy can be supported by a recent theoretical framework in the philosophy of medicine, which avoids the problems associated with the concepts of health and disease. (shrink)

The Neurological Disease Ontology (ND) is being developed to provide a comprehensive framework for the representation of neurological diseases (Diehl et al., 2013). ND utilizes the model established by the Ontology for General Medical Science (OGMS) for the representation of entities in medicine and disease (Scheuermann et al., 2009). The goal of ND is to include information for each disease concerning its molecular, genetic, and environmental origins, the processes involved in its etiology and realization, as well as its clinical presentation (...) including signs and symptoms. (shrink)

Chronic fatigue syndrome or myalgic encephalomyelitis remains a controversial illness category. This paper surveys the state of knowledge and attitudes about this illness and proposes that epistemic concerns about the testimonial credibility of patients can be articulated using Miranda Fricker’s concept of epistemic injustice. While there is consensus within mainstream medical guidelines that there is no known cause of CFS/ME, there is continued debate about how best to conceive of CFS/ME, including disagreement about how to interpret clinical studies of treatments. (...) Against this background, robust qualitative and quantitative research from a range of countries has found that many doctors display uncertainty about whether CFS/ME is real, which may result in delays in diagnosis and treatment for patients. Strikingly, qualitative research evinces that patients with CFS/ME often experience suspicion by health professionals, and many patients vocally oppose the effectiveness, and the conceptualization, of their illness as psychologically treatable. We address the intersection of these issues and healthcare ethics, and claim that this state of affairs can be explained as a case of epistemic injustice. We find evidence that healthcare consultations are fora where patients with CFS/ME may be particularly vulnerable to epistemic injustice. We argue that the marginalization of many patients is a professional failure that may lead to further ethical and practical consequences both for progressive research into CFS/ME, and for ethical care and delivery of current treatments among individuals suffering from this debilitating illness. (shrink)

Non-communicable diseases now account for the majority of the global burden of disease and an international campaign has emerged to raise their priority on the post-2015 development agenda. We argue, to the contrary, that there remain strong reasons to prioritize maternal and child health. Policy-makers ought to assign highest priority to the health conditions that afflict the worst off. In virtue of how little healthy life they have had, children who die young are among the globally worst off. Moreover, many (...) interventions to deal with the conditions that cause mortality in the young are low-cost and provide great benefits to their recipients. Consistent with the original Millennium Development Goals, the international community should continue to prioritize reductions in communicable diseases, neonatal conditions, and maternal health despite the shifts in the global burden of disease. (shrink)

This paper makes an important contribution to the ongoing debate over the validity of the psychological construct, ADHD. While not ruling out the possibility that something of value may lie at the core of this diagnosis, the authors articulate a clear set of problems with the research logic that forms the foundation of the disorder itself, reaching the conclusion that there appears to be insuffi cient, valid scientifi c evidence for the demarcation of a coherent and independent disease entity.

As Peter Niebyl has documented, one of the issues in which the Wittenberg-based physician and philosopher Daniel Sennert (1572–1637) departed from Paracelsus and his followers was the concept of disease. Paracelsus and some of his followers regarded diseases as real beings—so-called “disease-entities” (entia morbis) that can enter into the body of a living being and thereafter possess a clearly defined location in the affected organism. 1 For Sennert, such a view is a dangerous confusion between disease and its causes. According (...) to him, causes of disease can be present in an organism without actually causing a disease (Sennert 1629, p. 253). Moreover, he shares the traditional Christian doctrine .. (shrink)

Parkinson's Disease (PD) is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. The symptoms generally come on slowly over time. Early in the disease, the most obvious are shaking, rigidity, slowness of movement, and difficulty with walking. Doctors do not know what causes it and finds difficulty in early diagnosing the presence of Parkinson’s disease. An artificial neural network system with back propagation algorithm is presented in this paper for helping doctors in identifying (...) PD. Previous research with regards to predict the presence of the PD has shown accuracy rates up to 93% [1]; however, accuracy of prediction for small classes is reduced. The proposed design of the neural network system causes a significant increase of robustness. It is also has shown that networks recognition rates reached 100%. (shrink)

Parkinson's Disease (PD) is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. The symptoms generally come on slowly over time. Early in the disease, the most obvious are shaking, rigidity, slowness of movement, and difficulty with walking. Doctors do not know what causes it and finds difficulty in early diagnosing the presence of Parkinson’s disease. An artificial neural network system with back propagation algorithm is presented in this paper for helping doctors in identifying (...) PD. Previous research with regards to predict the presence of the PD has shown accuracy rates up to 93% [1]; however, accuracy of prediction for small classes is reduced. The proposed design of the neural network system causes a significant increase of robustness. It is also has shown that networks recognition rates reached 100%. (shrink)

I am a philosopher with Parkinson’s Disease. Over the past several years I’ve been trying to write about my situation. I wrote about how I was forced to face the disease. I described how the disease twists and distorts my world. Then I asked myself, as a philosophy writer and teacher, whether I could say anything that might help myself or others facing life with Parkinson’s? I found ideas in the ancient Stoics and expanded them with ideas about time, coming (...) up with suggestions for living as excellently as possible despite the disease. Looking at those suggestions, I realized how the special awareness, resolve, and attention I was suggesting would be eaten away by dementia, and I know that a majority of Parkinson’s patients face dementia if they survive long enough. What can philosophy say to me or any person whose self and philosophy are being erased? Writing about such issues has helped me deal with my situation, and maybe my essay might be useful for others dealing with decline. (shrink)

This chapter discusses several kinds of reduction that are often found in the biomedical sciences, in contrast to reduction in fields such as physics. This includes reduction as a methodological assumption for how to investigate phenomena like complex diseases, and reduction as a conceptual tool for relating distinct models of the same phenomenon. The case of Parkinson’s disease illustrates a wide variety of ways in which reductionism is an important tool in medicine.