Cardiovascular diseases (CVD) represent a significant cause of morbidity and mortality worldwide, necessitating early detection for effective intervention. This research explores the application of machine learning (ML) algorithms in predicting cardiovascular diseases with enhanced accuracy by integrating optimization techniques. By leveraging data-driven approaches, ML models can analyze vast datasets, identifying patterns and risk factors that traditional methods might overlook. This study focuses on implementing various ML algorithms, such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks, (...) optimized through techniques like hyperparameter tuning, cross-validation, and feature selection to improve prediction accuracy. (shrink)

Cardiovascular diseases (CVD) represent a significant cause of morbidity and mortality worldwide, necessitating early detection for effective intervention. This research explores the application of machine learning (ML) algorithms in predicting cardiovascular diseases with enhanced accuracy by integrating optimization techniques. By leveraging data-driven approaches, ML models can analyze vast datasets, identifying patterns and risk factors that traditional methods might overlook. This study focuses on implementing various ML algorithms, such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks, (...) optimized through techniques like hyperparameter tuning, cross-validation, and feature selection to improve prediction accuracy. (shrink)

The world's leading cause of morbidity and death is cardiovascular diseases (CVD), which makes early detection essential for successful treatments. This study investigates how optimization techniques can be used with machine learning (ML) algorithms to forecast cardiovascular illnesses more accurately. ML models can evaluate enormous datasets by utilizing data-driven techniques, finding trends and risk factors that conventional methods can miss. In order to increase prediction accuracy, this study focuses on adopting different machine learning algorithms, including Decision Trees, Random (...) Forest, Support Vector Machines, and Neural Networks, that have been tuned using strategies including hyper parameter selection, crossvalidation, and feature selection. (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)

The predictive capacity of the model is evaluated using evaluation measures, such as accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC-ROC). Our findings show that improved machine learning models perform better than conventional methods, offering trustworthy forecasts that can help medical practitioners with early diagnosis and individualized treatment planning. In order to achieve even higher predicted accuracy, the study's conclusion discusses the significance of its findings for clinical practice as well as future improvements that might be (...) made, like adding wearable device data in real-time or investigating deep learning techniques. (shrink)

The world's leading cause of morbidity and death is cardiovascular diseases (CVD), which makes early detection essential for successful treatments. This study investigates how optimization techniques can be used with machine learning (ML) algorithms to forecast cardiovascular illnesses more accurately. ML models can evaluate enormous datasets by utilizing data-driven techniques, finding trends and risk factors that conventional methods can miss. In order to increase prediction accuracy, this study focuses on adopting different machine learning algorithms, including Decision Trees, Random (...) Forest, Support Vector Machines, and Neural Networks, that have been tuned using strategies including hyper parameter selection, crossvalidation, and feature selection. (shrink)

Data preparation, feature engineering, model training, and performance evaluation are all part of the study methodology. To ensure reliable and broadly applicable models, we utilize optimization techniques like Grid Search and Genetic Algorithms to precisely adjust model parameters. Features including age, blood pressure, cholesterol levels, and lifestyle choices are employed as inputs for the machine learning models in the dataset, which consists of patient medical information. The predictive capacity of the model is evaluated using evaluation measures, such as accuracy, precision, (...) recall, F1-score, and the area under the ROC curve (AUC-ROC). Our findings show that improved machine learning models perform better than conventional methods, offering trustworthy forecasts that can help medical practitioners with early diagnosis and individualized treatment planning. In order to achieve even higher predicted accuracy, the study's conclusion discusses the significance of its findings for clinical practice as well as future improvements that might be made, like adding wearable device data in real-time or investigating deep learning techniques. (shrink)

The research methodology involves data preprocessing, feature engineering, model training, and performance evaluation. We employ optimization methods such as Genetic Algorithms and Grid Search to fine-tune model parameters, ensuring robust and generalizable models. The dataset used includes patient medical records, with features like age, blood pressure, cholesterol levels, and lifestyle habits serving as inputs for the ML models. Evaluation metrics, including accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC-ROC), assess the model's predictive power.

Purpose of Review Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is identified from Wuhan, China, and has spread almost worldwide. Recently, the newly identified SARS-CoV-2 has been confirmed to kill millions of people worldwide and is dangerous to society health, survival, and livelihood. The people with cardiovascular problems are noticed as most common patients of coronavirus disease 2019 (COVID-19). There is a greater risk of mortality and morbidity in these patients than other patients of COVID-19. In the heart, expressed (...) angiotensin-converting enzyme 2 (ACE2) and response effect of hyperactivity with angiotensin II associated to the renin-angiotensin mechanism are key factors of hypertension, atherosclerosis, and congestive heart failure. -/- Recent Findings Mortality rates have been observed about 10.5% cases in patients with cardiovascular disease; however, a mortality rate of 52% was recorded in patients with heart failure, while 12% recovered ultimately. The occupancy of intense injury controlled by troponin elevation was a noteworthy factor in relation to mortality. Among 187 patients infected with SARS-CoV-2, about 35% were diagnosed with cardiovascular disease (CVD) history and 28% with raised troponin. Troponin elevation was identified more frequently (55%) in patients with cardiovascular diseases. Mortality rate in patients without cardiovascular diseases and normal troponin was 7.6%, normal troponin and cardiovascular disease with 13.3%, augmented troponin and without cardiovascular disease 37.5%, however 69.4% among cardiovascular disease and advanced troponin. -/- Summary The study reflected a significant association of case fatality rate (CFR) to COVID-19 patients with cardiovascular diseases which supposed to be the most common dangerous risk factor and health challenge during the current pandemic situation. (shrink)

Data preparation, feature engineering, model training, and performance evaluation are all part of the study methodology. To ensure reliable and broadly applicable models, we utilize optimization techniques like Grid Search and Genetic Algorithms to precisely adjust model parameters. Features including age, blood pressure, cholesterol levels, and lifestyle choices are employed as inputs for the machine learning models in the dataset, which consists of patient medical information. The predictive capacity of the model is evaluated using evaluation measures, such as accuracy, precision, (...) recall, F1-score, and the area under the ROC curve (AUC-ROC). Our findings show that improved machine learning models perform better than conventional methods, offering trustworthy forecasts that can help medical practitioners with early diagnosis and individualized treatment planning. (shrink)

Cardiovascular diseases, including heart disease, pose a significant global health challenge, contributing to a substantial burden on healthcare systems and individuals. Early detection and accurate prediction of heart disease are crucial for timely intervention and improved patient outcomes. This research explores the potential of neural networks in predicting heart disease using a dataset collected from Kaggle, consisting of 1025 samples with 14 distinct features. The study's primary objective is to develop an effective neural network model for (...) binary classification, identifying the presence or absence of heart disease. The neural network architecture includes an input layer, a hidden layer, and an output layer, designed to capture intricate relationships within the dataset. Rigorous training and validation processes, accompanied by data preprocessing steps, ensure the model's robustness and generalization capabilities. The results demonstrate promising performance, with an accuracy of 92% and an average error of 0.062. Moreover, an analysis of feature importance highlights key predictors, including "oldpeak," "thalach," "trestbps," "ca," "thal," "cp," "chol," "sex," "restecg," "age," "slope," "fbs," and "exang." This research contributes to the field of predictive healthcare by leveraging neural networks to enhance heart disease prediction. The developed model offers the potential for early identification of individuals at risk, facilitating timely medical interventions and ultimately improving public health. Further exploration of machine learning techniques in healthcare promises to reshape disease prediction and prevention strategies. (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)

Non-communicable diseases (NCDs) such as cardiovascular diseases and diabetes are reported to have caused significant deaths for more than a decade. Consequently, NCDs have posed as a threat to the socio-economic well-being of individuals and families, contributed to a rise in healthcare costs and largely undermined the attainment of the Sustainable Development Goals (SDGs) especially in developing countries. According to the World Health Organization (WHO), the prevalence of NCDs have compounded the problem of already ill equipped healthcare systems in (...) these countries as they are faced with constraints to deal with the burden of both infectious and non-communicable diseases. Informed largely by the rapid increase in NCDs and their subsequent threat to public health, we aimed to ascertain the various healthcare interventions that Ghana has fashioned out in her bid to prevent and control the incidence of NCDs, how these interventions were rolled out and examined past and present barriers to their implementation since 1990. We culled the data gathered for this paper from both primary and secondary sources to construct a coherent synthesis and to facilitate discussions on Ghana’s NCDs interventions from 1990 to 2018. A systematic analysis of the data gathered, revealed that Ghana’s healthcare system has by far tackled the NCDs burden in two folds; the clinical care aspect and the health promotion aspect. While certain healthcare interventions were purposively directed toward addressing NCDs, others were directed at promoting healthy lifestyles but had a bearing on the prevention and control of NCDs. Present challenges concerning shortfalls in interventions are a reflection of unresolved challenges in the past. We argue that despite the significant strides made for more than two decades, the interventions have addressed the burden of NCDs with limited success given the trends in NCDs mortality and morbidity. (shrink)

The incidence of chronic non-communicable diseases (NCDs) such as diabetes, hypertension, cancers and cardiovascular diseases in Ghana has created a new mix of healthcare challenge for the country.

Today the whole world is facing a very difficult time due to corona virus which initially originated in Wuhan city of China. In China an unusual pneumonia was noticed earlier which later recognized as a pandemic. There have been two events in the past wherein crossover of animal corona viruses to humans has resulted in severe disease, one was SARS-CoV and the other was MERS-CoV. The genetic sequence of the COVID19 showed more than 80% similarities to SARS-CoV and 50% (...) to the MERSCoV. Corona viruses are enveloped positive sense single-stranded RNA virus belongs to a large family in which all strains of the family do not have virulent capacity. World Health Organisation has classified COVID-19 as a β CoV of group 2B. Structurally, SARS-CoV-2 has four main structural proteins and several accessory proteins through which it enters into the cell. This virus enters the through ACE2 receptors, which are found on various organs in human body. After entering into one body, it enters to another body via transmission through sneezing, coughing etc. People who have had diabetes, chronic respiratory disease, cardiovascular disease, or even high blood pressure and cancer are at higher risk of coronavirus. (shrink)

Air pollution is a pressing global issue, demanding swift and practical solutions across nations. Polluted air is not only harmful to physical health but also to mental well-being. Research has consistently shown that air pollution is linked to respiratory and cardiovascular problems, both in the short and long term. Short-term effects include coughing, asthma, and difficulty breathing, while long-term exposure can lead to chronic asthma, impaired lung function, cardiovascular diseases, and even death. Beyond physical health, air pollution can (...) also negatively impact mental well-being, contributing to depression, anxiety, and even suicide. -/- A recent study by a group of researchers led by Tomoaki Nakaishi, a researcher at Kyushu University, has gone further, examining how air pollution affects moral perception and unethical behavior. Unethical behavior refers to actions that are either illegal or deemed morally wrong by society. This study focuses on five types of unethical behavior: fatal actions (e.g., murder, terrorism, and suicide), assault (e.g., prostitution, rape, child abuse, and domestic violence), discrimination (e.g., gender and racial discrimination), financial misconduct (e.g., fraud and bribery/corruption), and organized misconduct. (shrink)

Type 2 diabetes mellites (T2DM) is a common complex disease with multiple factors contributing to its development and progression. Dyslipidemia refers to the abnormality of lipid metabolism, characterized by elevated levels of low-density lipoprotein (LDL), total cholesterol (TC), triglycerides (TG), and decreased levels of high-density lipoprotein (HDL). It is a major risk factor for cardiovascular disease in type 2 diabetic patients. This study aimed to evaluate the diagnostic value of glycosylated hemoglobin (HbA1c) and fasting blood glucose (FBG) (...) in predicting the risk of developing diabetic dyslipidemia. 86 clinically diagnosed Libyan patients with type 2 diabetes mellitus (44 males: 42 females), 30 years and 70 years of age were engaged in this study. Levels of HbA1C, blood glucose, TC, TG, LDL, and HDL were performed using a Lntegra Cobas 400+ analyzer. The findings showed that HbA1c exhibits a significant positive correlation with TC and TG with no significant correlation with LDL and HDL levels. Thus, this study suggests that HbA1c can be used as a potential dual marker of glycemic control and dyslipidemia in type 2 diabetes mellitus. (shrink)

Abstract Heart Attack is the Cardiovascular Disease (CVD) which causes the most deaths among CVDs. We collected a dataset from Kaggle website. In this paper, we propose an ANN model for the predicting whether a patient has a heart attack or not that. The dataset set consists of 9 features with 1000 samples. We split the dataset into training, validation, and testing. After training and validating the proposed model, we tested it with testing dataset. The proposed model reached (...) an accuracy of 98.01% on the Heart Disease Dataset. (shrink)

Heart palpitations, while often benign, can sometimes be indicative of severe underlying conditions requiring immediate intervention. Accurate and swift diagnosis thus remains a clinical priority. "A CLIPS-Based Expert System for Heart Palpitations Diagnosis" represents a novel approach to addressing this challenge, harnessing the power of artificial intelligence and rule-based expert systems. Specifically, this system applies a suite of 7 if-then rules to evaluate potential heart palpitations causes and assign one of three outcomes: 1) A confirmed diagnosis of heart palpitations, 2) (...) A suspected link to cardiovascular diseases, and 3) A possible association with anxiety or stress disorders. The expert system offers an intuitive user interface, allowing for seamless symptom input and instant diagnosis based on user-provided information. This paper explores the various phases of this expert system's lifecycle, including design, implementation, and evaluation. Furthermore, the study situates the system within the broader discourse on rule-based expert systems for heart palpitations diagnosis, critically analyzing their efficiency, potential pitfalls, and ongoing challenges. Through this research, the value of integrating rule-based expert systems in clinical diagnostic processes is highlighted, illustrating its capacity to enhance diagnostic accuracy and patient outcomes. (shrink)

Patients with schizophrenia have a higher risk of cardiovascular diseases and higher mortality from them than does the general population; however, the underlying mechanism remains unclear. Impaired cerebral autoregulation is associated with cerebrovascular diseases and their mortality. Increased or decreased cerebral blood flow in different brain regions has been reported in patients with schizophrenia, which implies impaired cerebral autoregulation. This study investigated the cerebral autoregulation in 21 patients with schizophrenia and 23 age- and sex-matched healthy controls. None of the (...) participants had a history of cardiovascular diseases, hypertension, or diabetes. All participants underwent 10-min blood pressure and cerebral blood flow recording through finger plethysmography and Doppler ultrasonography, respectively. Cerebral autoregulation was assessed by analyzing two autoregulation indices: the mean blood pressure and cerebral blood flow correlation coefficient (Mx), and the phase shift between the waveforms of blood pressure and cerebral blood flow determined using transfer function analysis. Compared with the controls, the patients had a significantly higher Mx (0.257 vs. 0.399, p = 0.036) and lower phase shift (44.3° vs. 38.7° in the 0.07–0.20 Hz frequency band, p = 0.019), which indicated impaired maintenance of constant cerebral blood flow and a delayed cerebrovascular autoregulatory response. Impaired cerebral autoregulation may be caused by schizophrenia and may not be an artifact of coexisting medical conditions. The mechanism underlying impaired cerebral autoregulation in schizophrenia and its probable role in the development of cerebrovascular diseases require further investigation. (shrink)

All ordinary decisions involve some risk. If I go outside for a walk, I may trip and injure myself. But if I don’t go for a walk, I slightly increase my chances of cardiovascular disease. Typically, we disregard most small risks. When, for practical purposes, is it appropriate for one to ignore risk? This issue looms large because many activities performed by those in wealthy societies, such as driving a car, in some way risk contributing to climate harms. (...) Are these activities morally appropriate? -/- In this paper, I first summarize and respond to some arguments that purport to show that it is appropriate to ignore or discount very small risks. I argue that because our rationality is bounded, it is impossible for us to include every small risk in our decision-making process, and so we may reasonably use heuristics to guide many decisions. However, contrary to some thinkers, I argue that this does not violate the spirit of expected value theory; it merely shows that we should adopt a so-called "two-level" view. Our use of heuristics allows for the reasonable ignoring of some risks, and this perhaps explains why one might be inclined to think that individual climate-related risks are negligible. However, virtually all greenhouse-gas emitting activities in fact have some climate risk on the negative side of the ledger, and the use of heuristics does not permit the general ignoring of climate-change-related risk by individuals on grounds of expediency of judgment and decision-making. (shrink)

Background Previous analyses of democracy and population health have focused on broad measures, such as life expectancy at birth and child and infant mortality, and have shown some contradictory results. We used a panel of data spanning 170 countries to assess the association between democracy and cause-specific mortality and explore the pathways connecting democratic rule to health gains. -/- Methods We extracted cause-specific mortality and HIV-free life expectancy estimates from the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (...) and information on regime type from the Varieties of Democracy project. These data cover 170 countries and 46 years. From the Financing Global Health database, we extracted gross domestic product (GDP) per capita, also covering 46 years, and Development Assistance for Health estimates starting from 1990 and domestic health spending estimates starting from 1995. We used a diverse set of empirical methods—synthetic control, within-country variance decomposition, structural equation models, and fixed-effects regression—which together provide a robust analysis of the association between democratisation and population health. -/- Findings HIV-free life expectancy at age 15 years improved significantly during the study period (1970–2015) in countries after they transitioned to democracy, on average by 3% after 10 years. Democratic experience explains 22·27% of the variance in mortality within a country from cardiovascular diseases, 16·53% for tuberculosis, and 17·78% for transport injuries, and a smaller percentage for other diseases included in the study. For cardiovascular diseases, transport injuries, cancers, cirrhosis, and other non-communicable diseases, democratic experience explains more of the variation in mortality than GDP. Over the past 20 years, the average country's increase in democratic experience had direct and indirect effects on reducing mortality from cardiovascular disease (−9·64%, 95% CI −6·38 to −12·90), other non-communicable diseases (−9·14%, −4·26 to −14·02), and tuberculosis (−8·93%, −2·08 to −15·77). Increases in a country's democratic experience were not correlated with GDP per capita between 1995 and 2015 (ρ=–0·1036; p=0·1826), but were correlated with declines in mortality from cardiovascular disease (ρ=–0·3873; p<0·0001) and increases in government health spending (ρ=0·4002; p<0·0001). Removal of free and fair elections from the democratic experience variable resulted in loss of association with age-standardised mortality from non-communicable diseases and injuries. -/- Interpretation When enforced by free and fair elections, democracies are more likely than autocracies to lead to health gains for causes of mortality (eg, cardiovascular diseases and transport injuries) that have not been heavily targeted by foreign aid and require health-care delivery infrastructure. International health agencies and donors might increasingly need to consider the implications of regime type in their efforts to maximise health gains, particularly in the context of ageing populations and the growing burden of non-communicable diseases. (shrink)

Despite analgesic and emetogenic benefits, cannabis has been banned from prescription in a number of western countries. Although some benefits are shared by drugs already available, the options of prescription are limited to the physician. The negative side-effects of cannabis do not justify this limitation on freedom and autonomy. Recreational use warrants limitations, as the search for euphoria is regularly believed to be a non-autonomous behavior. Medical prescriptions serve an analgesic and emetogenic purpose comparable to other prescribed drugs. This vindicates (...) the right for a physician to prescribe cannabis to qualified patients. For a patient to be considered qualified, they must be an adult that does not drive often, is not pregnant, and not at risk of schizophrenia or cardiovascular disease. If possible, cannabis should be eaten in an effort to avoid smoke irritation in the lungs and aerodigestive tract. In the end, the question is not whether cannabis use is efficable, but rather is the limitation of medical treatments to patients in need ethical? In light of the medical benefits, the banning of cannabis is simply immoral. Keywords: cannabis, banned substance, prescription, analgesic, emetogenic Note. Authors did not receive any external funding, nor display conflict of interest. (shrink)

Coronavirus disease (Covid-19) is an infectious disease caused by the SARS-CoV-2 virus. Spreading rate of mutated corona virus (delta variant) during second wave was very fast. Most of the people infected with the COVID-19 virus experienced mild to moderate to severe respiratory illness. Although patients in the second wave were younger but the duration of hospitalization and case fatality rate were lower than those in the first wave. During first wave of Covid-19 it was observed that persons above (...) 55 years of age and those with underlying medical problems like cardiovascular disease, diabetes, chronic respiratory disease and cancer were more susceptible to develop serious illness but in second it was observed that person between 30 to 55 years along with old age and younger people suffering from hypertension, diabetes and respiratory diseases were more susceptible to this covid-19 virus. The most frequent signs and symptoms in both waves were fever, dyspnea, pneumonia, and cough, and the most relevant comorbidities were cardiovascular diseases, type 2 diabetes mellitus, and chronic neurological diseases. Every country has lost its human resources due to covid-19 virus which not only disturb the social life but economic conditions are also very badly affected. Now we use the resources so rapidly that may create economic imbalance in future. The psychological effect is an essential component of disaster management of infectious pandemics. (shrink)

The COVID-19 pandemic is a major global challenge. There are several risk factors associated with mortality in patients with COVID-19, including age, gender, diabetes mellitus, cerebrovascular, cardiovascular, and pulmonary diseases. Among these factors, patients with cardiovascular disease, diabetes mellitus, and obesity have the highest mortality rates. This paper aims to review how adjusting food and nutrition can help reduce the risk of obese patients contracting COVID-19. Various literature sources were examined, including studies on the genetics of obesity (...) and the consequences it can have, such as diabetes, heart and vascular diseases, cancer, trauma, and infection. Consequently, the role of nutrition in reducing the risk of obese patients being exposed to COVID-19 was described. It is recommended that obese patients strictly adhere to a diet that includes adequate and balanced food intake in order to reduce their risk of contracting COVID-19. (shrink)

Ayurveda is the science of living being. With the aim of health and procurement of disease it almost covers all facets of life. It includes health of an individual at physical, mental, spiritual, social level. Ayurvedic classics includes brihatrayee samhita like Charak, Sushruta and Ashtanga Hridaya. A review based study of music (geet, sangeet) was done in these classics to explore whether these classics includes any form of music as therapy or not. Based on review of these classics it (...) was found that music in form of geet (vocal or instrument) has been given in contexts like vajikara(aphrodisiac), vata/pitta prakriti (basic consititutional make up), gandharva sattva, madyapana (intake of alcohol), therapy for rajyakshma (immune deficiency diseases), sanyasa (syncope/coma), jivadan (haemetemesis) etc. various swara have been correlated with their pacifying power of three sharirikadoshas viz. vata, pitta and kapha. Also a relationship between various raga of indian classical music have been said to be based on time of its singing which may be used as a therapy to pacify the dearrangement of prevalent doshas. several articles have shown music as therapy in sleep disorder, psychiatric disorders, schizophrenia, cardiovascular, cancer etc. with this paper a perspective of Ayurveda have been explored in music therapeutics. (shrink)

The public health crisis is started with emergence of new coronavirus on 11 February 2020 which triggered as coronavirus disease-2019 (COVID-19) pandemics. The causative agent in COVID-19 is made up of positively wrapped single-stranded RNA viruses ~ 30 kb in size. The epidemiology, clinical features, pathophysiology, and mode of transmission have been documented well in many studies, with additional clinical trials are running for several antiviral agents. The spreading potential of COVID-19 is faster than its two previous families, the (...) severe acute respiratory syndrome coronavirus (SARS-CoV) and the Middle East respiratory syndrome coronavirus (MERS-CoV). Apart from clinical manifestation, comorbid status is playing key role for prevalence of COVID-19 infection and mortalities. The comorbid effects associated with COVID-19 are diabetes, cardiovascular, digestive, hepatitis-B, cerebrovascular, hypertension, liver injury, coronary heart disease, cancer, rheumatoid arthritis, and neurological impairment. Antimalarial drugs (chloroquine and hydroxychloroquine), remdesivir, Tocilizumab, clopinavir/ritonavir, convalescent plasma therapy, spike protein-angiotensin-converting enzyme 2 (ACE2) inhibitors, human monoclonal antibodies, mRNA-1273, mesenchymal stem cells, Indian and Chinese traditional medicine, small molecules antioxidant, natural products and dietary supplements, high doses of vitamin-E, -C, -D, minerals, flavonoids,and IFN-beta are therapeutic intervention running to develop treatment against COVID-19. Although clinical usage of these therapeutic agents against COVID-19 is well documented, cytokine storms, absence of appropriate animal model have limited its therapeutic use. This review explores the clinical information currently available on COVID-19 on the mechanisms of infection, prevention, management, comorbid status, and current drug treatment options. (shrink)

A narrative review of the major evidence concerning the relationship between emotional regulation and depression was conducted. The literature demonstrates a mediating role of emotional regulation in the development of depression and physical illness. Literature suggests in fact that the employment of adaptive emotional regulation strategies (e.g., reappraisal) causes a reduction of stress-elicited emotions leading to physical disorders. Conversely, dysfunctional emotional regulation strategies and, in particular, rumination and emotion suppression appear to be influential in the pathogenesis of depression and physiological (...) disease. More specifically, the evidence suggests that depression and rumination affect both cognitive (e.g., impaired ability to process negative information) and neurobiological mechanisms (e.g., hypothalamic pituitary adrenal axis overactivation and higher rates of cortisol production). Understanding the factors that govern the variety of health outcomes that different people experience following exposure to stress has important implications for the development of effective emotion-regulation interventional approaches (e.g., mindfulness-based therapy, emotion-focused therapy, and emotion regulation therapy). (shrink)

Myocardial geometric remodeling is a response to increased stress which includes increased afterload situations during clinical conditions. In this review, we have focused on early and late geometric features in aortic stenosis, importance of recognition of these findings and consequences due to progression of valve disease. We have also pointed out the similarities in early focal and global myocardial geometric remodeling in acute and chronic conditions as hypertension and acute stress cardiomypathy which are associated with myocardial functional and geometric (...) response to acute or chronic stress exposure and relevant increased afterload. In aortic stenosis, target organ involvement in disease progression has been evaluated and discussed in the report. In addition to quantitative evaluation of valve disease, importance of myocardial involvement and global assessment of patients with aortic stenosis also have been mentioned in the report. Finally, we have discussed the importance of global myocardial geometric changes and timing for surgery before development of heart failure in this specific group of patients. (shrink)

Rare diseases pose a particular priority setting problem. The UK gives rare diseases special priority in healthcare priority setting. Effectively, the National Health Service is willing to pay much more to gain a quality-adjusted life-year related to a very rare disease than one related to a more common condition. But should rare diseases receive priority in the allocation of scarce healthcare resources? This article develops and evaluates four arguments in favour of such a priority. These pertain to public values, (...) luck egalitarian distributive justice the epistemic difficulties of obtaining knowledge about rare diseases and the incentives created by a higher willingness to pay. The first is at odds with our knowledge regarding popular opinion. The three other arguments may provide a reason to fund rare diseases generously. However, they are either overinclusive because they would also justify funding for many non-rare diseases or underinclusive in the sense of justifying priority for only some rare diseases. The arguments thus fail to provide a justification that tracks rareness as such. There are no data in this work. (shrink)

Debates about the genuine disease status of controversial diseases rely on intuitions about a range of factors. Adopting tools from experimental philosophy, this paper explores some of the factors that influence judgments about whether low sexual desire should be considered a disease and whether it should be medically treated. Drawing in part on some assumptions underpinning a divide in the literature between viewing low sexual desire as a genuine disease and seeing it as improperly medicalized, we investigate (...) whether health and disease judgments are affected by factors such as an individual’s gender, the cause of the low desire, whether the desire is high or low, and both personal and societal valuations of the condition. Our main findings indicate that (a) the cause of a condition influences whether it is judged a disorder, (b) how the individual values the condition influences whether the condition is seen as a proper target of medical intervention, and (c) perceived dysfunction influences judgments regarding health, disorder classification, medicalization, and medical intervention. Our findings help further illuminate the intricate interplay of factors that influence judgments about health and disease in controversial conditions. (shrink)

Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and (...) information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain. (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)

In the contemporary biomedical literature, every disease is considered genetic. This extension of the concept of genetic disease is usually interpreted either in a trivial or genocentrist sense, but it is never taken seriously as the expression of a genetic theory of disease. However, a group of French researchers defend the idea of a genetic theory of infectious diseases. By identifying four common genetic mechanisms (Mendelian predisposition to multiple infections, Mendelian predisposition to one infection, and major gene (...) and polygenic predispositions), they attempt to unify infectious diseases from a genetic point of view. In this article, I analyze this explicit example of a genetic theory, which relies on mechanisms and is applied only to a specific category of diseases, what we call “a regional genetic theory.” I have three aims: to prove that a genetic theory of disease can be devoid of genocentrism, to consider the possibility of a genetic theory applied to every disease, and to introduce two hypotheses about the form that such a genetic theory could take by distinguishing between a genetic theory of diseases and a genetic theory of Disease. Finally, I suggest that network medicine could be an interesting framework for a genetic theory of Disease. (shrink)

In this paper, we identify some key features of what makes something a disease, and consider whether these apply to pregnancy. We argue that there are some compelling grounds for regarding pregnancy as a disease. Like a disease, pregnancy affects the health of the pregnant person, causing a range of symptoms from discomfort to death. Like a disease, pregnancy can be treated medically. Like a disease, pregnancy is caused by a pathogen, an external organism invading (...) the host’s body. Like a disease, the risk of getting pregnant can be reduced by using prophylactic measures. We address the question of whether the ‘normality’ of pregnancy, its current necessity for human survival, or the value often attached to it are reasons to reject the view that pregnancy is a disease. We point out that applying theories of disease to the case of pregnancy, can in many cases illuminate inconsistencies and problems within these theories. Finally, we show that it is difficult to find one theory of disease that captures all paradigm cases of diseases, while convincingly excluding pregnancy. We conclude that there are both normative and pragmatic reasons to consider pregnancy a disease. (shrink)

Some ‘naturalist’ accounts of disease employ a biostatistical account of dysfunction, whilst others use a ‘selected effect’ account. Several recent authors have argued that the biostatistical account offers the best hope for a naturalist account of disease. We show that the selected effect account survives the criticisms levelled by these authors relatively unscathed, and has significant advantages over the BST. Moreover, unlike the BST, it has a strong theoretical rationale and can provide substantive reasons to decide difficult cases. (...) This is illustrated by showing how life-history theory clarifies the status of so-called diseases of old age. The selected effect account of function deserves a more prominent place in the philosophy of medicine than it currently occupies. _1_ Introduction _2_ Biostatistical and Selected Effect Accounts of Function _3_ Objections to the Selected Effect Account _3.1_ Boorse _3.2_ Kingma _3.3_ Hausman _3.4_ Murphy and Woolfolk _4_ Problems for the Biostatistical Account _4.1_ Schwartz _5_ Analysis versus Explication _6_ Explicating Dysfunction: Life History Theory and Senescence _7_ Conclusion. (shrink)

Our goal in this paper is to articulate a precise concept of at least a certain kind of disease-mongering, showing how pharmaceutical marketing can commercially exploit certain diseases when their best definition is given through the success of a treatment in a clinical trial. We distinguish two types of disease-mongering according to the way they exploit the definition of the trial population for marketing purposes. We argue that behind these two forms of disease-mongering there are two well-known (...) problems in the statistical methodology of clinical trials (the reference class problem and the distinction between statistical and clinical significance). Overcoming them is far from simple. (shrink)

Similarly to other accounts of disease, Christopher Boorse’s Biostatistical Theory (BST) is generally presented and considered as conceptual analysis, that is, as making claims about the meaning of currently used concepts. But conceptual analysis has been convincingly critiqued as relying on problematic assumptions about the existence, meaning, and use of concepts. Because of these problems, accounts of disease and health should be evaluated not as claims about current meaning, I argue, but instead as proposals about how to define (...) and use these terms in the future, a methodology suggested by Quine and Carnap. I begin this article by describing problems with conceptual analysis and advantages of “philosophical explication,” my favored approach. I then describe two attacks on the BST that also question the entire project of defining “disease.” Finally, I defend the BST as a philosophical explication by showing how it could define useful terms for medical science and ethics. (shrink)

Response to commentary. We are grateful to Crockett and Craigie for their interesting remarks on our paper. We accept Crockett’s claim that there is a need for caution in drawing inferences about patient groups from work on healthy volunteers in the laboratory. However, we believe that the evidence we cited established a strong presumption that many of the patients who are routinely taking a medication, including many people properly prescribed the medication for a medical condition, have morally significant aspects of (...) their cognition and behavior modified in a way that is unintended and may sometimes be unwelcome. Crockett notes that in some cases the effects of long-term drug use may differ, sometimes markedly, from the effects of short-term use. However, if acute use of a drug affects a neural system involved in mediating moral cognition or behavior, this nevertheless provides some evidence that chronic use of the drug may affect that same system and thus have morally significant effects. It is also plausible, in some cases, that an acute moral effect would give rise to a chronic moral effect via cognitive mechanisms. ... (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)

There are many eye diseases but the most two common retinal diseases are Age-Related Macular Degeneration (AMD), which the sharp, central vision and a leading cause of vision loss among people age 50 and older, there are two types of AMD are wet AMD and DRUSEN. Diabetic Macular Edema (DME), which is a complication of diabetes caused by fluid accumulation in the macula that can affect the fovea. If it is left untreated it may cause vision loss. Therefore, early detection (...) of diseases is a critical importance. Our main goal is to help doctors detect these diseases quickly before reaching a late stage of the disease. In ophthalmology, optical coherence tomography (OCT) is critical for evaluating retinal conditions. OCT is an imaging technique used to capture high-resolution cross-sections of the retinas of patient. In this thesis, we review ways and techniques to use deep learning classification of the optical coherence tomography images of diseases from which a Retinal is suffering. The models used to improve patient care are (VGG-16, MobileNet, ResNet-50, Inception V3, and Xception) to reduce costs and allow fast and reliable analysis in large studies. The obtained results are encouraging, since the best model ResNet-50 reaching 96.21% of testing accuracy, which is very useful for doctors, to diagnose retinal diseases. (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)

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)

This paper addresses the use of dispositions in the Infectious Disease Ontology (IDO). IDO is an ontology constructed according to the principles of the Open Biomedical Ontology (OBO) Foundry and uses the Basic Formal Ontology (BFO) as an upper ontology. After providing a brief introduction to disposition types in BFO and IDO, we discuss three general techniques for representing combinations of dispositions under the headings blocking dispositions, complementary dispositions, and collective dispositions. Motivating examples for each combination of dispositions is (...) given along with a specific use case in IDO. Description logic restrictions are used to formalize statements relating to these combinations. (shrink)

Whipple disease is a rare, infectious, disease first described from a single case by Whipple in 1907. As well as characterising the clinical and pathological features of the condition, Whipple made two suggestions regarding its aetiology. These were either than the disease was caused by an infectious agent, or that it was of metabolic origin. As the disease is now thought to be caused by infection with the bacterium Tropheryma whipplei, historical reviews of the history of (...) the disease typically mention only the first of these suggestions. In this paper, we therefore revisit Whipple’s other theory. We argue that a diverse and often successful research programme was developed around this mechanism of disease causation which gave rise to many useful findings on the condition. In the later parts of this article, we then turn to discuss the surprising neglect of this period of Whipple disease research in the current literature, and conclude by offering a brief reconstruction of this early history suitable for use in a technical context. (shrink)

This review was conducted to examine the causes, diagnoses, clinical manifestations, and available treatments for Graves' disease. Keywords like "Graves' disease," "radioactive iodine," "etiology," and "treatment" were used to search for data pertaining to Textbooks on endocrinology and other papers from these sources were also located. The introduction, etiology, risk factors, symptoms, diagnosis, course of treatment, and the contribution of many factors to the beginning of Graves' disease are all covered in this review article.

On 11 March 2020, the World Health Organization declared a pandemic of the COVID-19 coronavirus disease that was first recognized in China in late 2019. Among the primary effects caused by the pandemic, there was the dissemination of health preventive measures such as physical distancing, travel restrictions, self-isolation, quarantines, and facility closures. This includes the global disruption of socio-economic systems including the postponement or cancellation of various public events (e.g., sporting, cultural, or religious), supply shortages and fears of the (...) same, schools and universities closure, evacuation of foreign citizens, a rise of unemployment, changes in the international aid schemes, misinformation, and incidents of discrimination toward people affected by or suspected of having the COVID-19 disease. The pandemic has brought to the fore unpreparedness in critical areas that require attention, amid prospects and challenges. Moreover, considerable reorganization efforts are required with implications for assets, resources, norms, and value systems. COVID-19 is challenging the concept of globalization and stimulating responses at the levels of local and regional socio-economic systems that lead to the mobilization of assets that have been unrecognized earlier on, such as various forms of economic capital, social capital, cultural capital, human capital, and creative capital. For example, through digital channels, local groups are forming to create schemes of support for physical and mental wellbeing. These emerging exchanges lead to various social and technological innovations by building on skills and assets that are less important in the free-market economy, such as empathy, skills for crafts, making and fixing; locally grown microgreens; and micromanufacturing. Isolation and local living are also making it much harder to ignore the civic responsibilities towards communities, meant as individuals, vulnerable groups, and local businesses. Whilst the pandemic is limiting physical participation, this challenging time is uncovering alternative ways of mutual support, which may create long-term benefits for socio-economic systems, including environmental and biodiversity protection, reduction of the air pollution, and climate action. The pandemic’s threat to public health will hopefully be overcome with implications for disruption for an extended period that we are unable to forecast at this stage. It is key to focus on studies recognizing the activities and interventions leading to the recovery of socio-economic systems after the pandemic. Reflecting and planning on how societies and economies will go back to “business as usual” requires new forms of communication and cooperation, imaginative design thinking, new styles of management, as well as new tools and forms of participation in various public policies. Many questions related to the care of the vulnerable, economic restart, and the risk of future pandemics, to mention but a few, are already occupying the academic, scientific, experts, and activist communities, who have started to imagine the “new normal.”. (shrink)

We recently suggested that there are both pragmatic and normative reasons to classify pregnancy as a disease. Several scholars argued against our claims. In this response, we defend the disease view of pregnancy against their criticism. We claim that the dysfunctional account of disease that some of our critics rely on has some counterintuitive results. Furthermore, we claim that our critics assume what needs to be argued that the primary function of our sexual organs is to reproduce. (...) Since only a small percentage of sexual intercourse leads to pregnancy, it is far from obvious that reproduction is the primary biological function of our sexual organs. We also claim that while taking pregnancy itself as a reference class could avoid the conclusion that pregnancy is a disease, the strategy is problematic since it renders the Boorsean approach to disease and health circular and effectively deprives it of any utility in determining whether a particular phenomenon is a disease or not. (shrink)

In this article, I will reconstruct the monocausal model and argue that modern 'multifactorial diseases' are not monocausal by definition. 'Multifactorial diseases' are instead defined according to a constitutive disease model. On closer analysis, infectious diseases are also defined using the constitutive model rather than the monocausal model. As a result, our classification models alone cannot explain why infectious diseases have a universal etiology while chronic and noncommunicable diseases lack one. The explanation is instead provided by the nineteenth-century germ (...) theorists. (shrink)

Brain-related diseases are among the most difficult diseases due to their sensitivity, the difficulty of performing operations, and their high costs. In contrast, the operation is not necessary to succeed, as the results of the operation may be unsuccessful. One of the most common diseases that affect the brain is Alzheimer’s disease, which affects adults, a disease that leads to memory loss and forgetting information in varying degrees. According to the condition of each patient. For these reasons, it (...) is important to classify memory loss and to know the patient at what level and his assessment of Alzheimer's disease through CT scans of the brain. In this thesis, we review ways and techniques to use deep learning classification to classifying the Alzheimer's Disease The proposed method used to improve patient care, reduce costs, and allow fast and reliable analysis in large studies. The model will be designed using Python language for implementing the system, which is very useful for doctors, classifying the Alzheimer's Disease, was used. The model used 70% from image for training and 30% from image for validation, our trained model achieved an accuracy of 100% on a held-out test set. (shrink)