Heart Diseases Prediction with Data Mining and Neural Network Techniques

The Framingham Heart Study has contributed importantly to understanding of the causes of coronary heart disease (CHD), stroke, and other cardiovascular diseases. Framingham research has helped define the quantitative and additive nature of these causes or, as they are now called, “cardiovascular risk factors.”1 The National Cholesterol Education Program (NCEP)2 3 has made extensive use of Framingham data in developing its strategy for preventing CHD by controlling high cholesterol levels. The NCEP guidelines2 3 adjust the intensity of cholesterol-lowering therapy with absolute risk as determined by summation of risk factors. The National High Blood Pressure Education Program (NHBPEP) has set forth a parallel approach for blood pressure control. In contrast to the NCEP,2 however, earlier NHBPEP reports issued through the Joint National Committee4 did not match the intensity of therapy to absolute risk for CHD. “Normalization” of blood pressure is the essential goal of therapy regardless of risk status. Blood pressure–lowering therapy is carried out as much for prevention of stroke and other cardiovascular complications as for reduction of CHD risk. Nonetheless, risk assessment could be important for making decisions about type and intensity of therapy for hypertension. Thus, the most recent Joint National Committee report5 gives more attention to risk stratification for adjustment of therapy for hypertension. Although Framingham data have already been influential in the development of national guidelines for risk factor management, the opportunity may exist for both cholesterol and blood pressure programs to draw more extensively from Framingham results when formulating improved risk assessment guidelines and recommending more specific strategies for risk factor modification. The American Heart Association has previously used Framingham risk factor data to prepare charts for estimating CHD risk. Framingham investigators of the National Heart, Lung, and Blood Institute prepared the original charts and have now revised …

Heart Disease and Stroke Statistics—2009 Update

BackgroundUnderstanding how risk factors (tobacco, alcohol, physical inactivity, unhealthy diet, high blood pressure, and high cholesterol) change over time is a critical aim of public health. The associations across the social gradient over time are important considerations. Risk factor surveillance systems have a part to play in understanding the epidemiological distribution of the risk factors so as to improve preventive measures and design public health interventions for reducing the burden of disease.MethodsRepresentative, cross-sectional data were collected in South Australia using telephone interviews, conducted on a minimum of 600 randomly selected people (of all ages) each month. Data were collected from January 2004 to December 2013. Unadjusted prevalence over time, the relative percentage change over the 10 years, and the absolute change of the risk factors with sex, age group, and socio-economic status (SES) estimates are presented.ResultsIn total 55,548 adults (≥18 years) were interviewed (mean age = 47.8 years, 48.8% male). Decreases were apparent for insufficient physical activity, inadequate fruit and vegetables, smoking, and soft drink consumption of ≥500 ml/day. Increases were found over the 10 years for obesity, high cholesterol, diabetes, and for those with no risk factors. Apparent differences were noticeable by different sex, age, and SES categories. While increases in physical activity and fruit and vegetable consumption and decreases in smoking prevalence and multiple risk factors are to be expected in 2020–2021, the prevalence of obesity, high blood pressure, high cholesterol, and diabetes are expected to increase.ConclusionsPublic health efforts in increasing the proportion of the population undertaking appropriate risk factor behavior are showing signs of success, with data from 2004 to 2013 showing encouraging trends. Deriving comparable trends over time by key demographics and SES variables provides evidence for policymakers and health planners to encourage interventions aimed at preventing chronic disease.Electronic supplementary materialThe online version of this article (doi:10.1186/s12963-014-0031-z) contains supplementary material, which is available to authorized users.

Cardiovascular sickness is the largest cause of death in developing countries. The study of this coronary heart disease prediction model using a data mining technique and decision tree algorithm are applied in medical research, especially in heart disease prediction. The Coronary Heart Disease is also known as Coronary Artery Disease (CAD). Hence the decision system is analyses the heart disease for the patient. In this paper coronary illness studied a more number of input attributes and database records based on the patient's clinical data. This paper focuses on the around the prediction of heart disease accuracy value using the decision tree algorithm.

Objective: To study knowledge, attitude, practice and behavior about CVD in collegeand university students of Peshawar. Methodology: This cross sectional study was conducted in colleges and universitystudents from October 2015 to May 2016. A questionnaire based on WHO stepwiseapproach to surveillance was used to study the understanding of knowledge, attitudeand practice/behavior towards cardiovascular health and barriers to healthy practicesbesides demographic data. The questionnaires were distributed among studentsthrough administration of the institute. Data was analyzed using SPSS 16. Results: A total of 618 participants were included. The response rate was 61.8%.Among these, 43.5 % were males. Mean age of students was 20.58 ± 2.16 years.Knowledge of risk factors of CVD was documented as high blood pressure 77.2%,diabetes 34.3%, high cholesterol 77.5%, physical inactivity 41.1%,overweight 66.7%,44.3% smoking 44.3%, mental stress 57%. Of respondents 69.9% knew pain in chestas a major heart attack warning sign. Respondents familiar with one sign of heart attackwere 50%, two signs 37%, three signs 11.42%, four signs 2.5%. Consumption of morefruits and vegetables and exercise can prevent heart disease was known to 85.1%and82.1% respectively. As regarding practice, 59.5% of the respondents had their bloodpressure measured, 75% took healthy food and reduced their consumption of unhealthyfoods, 44.2% tried to lose weight, 67.5% tried to increase physical activity, 50% ofsmokers said that they had attempted to quit smoking. Conclusion: College students had satisfactory knowledge of CVD, its risk factors,warning signs, heart healthy diet and physical activity. However, knowledge has to beput into practice to create positive attitude and good practice. Key Words: CVD, KAP study, Risk factors, Prevention

Data mining techniques have been widely used in clinical decision support systems for prediction and diagnosis of various diseases with good accuracy. These techniques have been very effective in designing clinical support systems because of their ability to discover hidden patterns and relationships in medical data. One of the most important applications of such systems is in diagnosis of heart diseases because it is one of the leading causes of deaths all over the world. Almost all systems that predict heart diseases use clinical dataset having parameters and inputs from complex tests conducted in labs. None of the system predicts heart diseases based on risk factors such as age, blood pressure, fasting blood sugar, chest pain etc. Heart disease patients have lot of these visible risk factors in common which can be used very effectively for diagnosis. System based on such risk factors would not only help medical professionals but it would give patients a warning about the probable presence of heart disease even before he visits a hospital or goes for costly medical check-ups. Hence this paper presents a technique for prediction of heart disease using major risk factors. This technique involves two most successful data mining tools, Support vector machine and Principal component analysis. The hybrid system implemented uses the global optimization advantage PCA for initialization of neural network weights. The learning is fast, more stable and accurate as compared to back propagation.

Heart disease, alternatively known as cardiovascular disease, encases various conditions that impact the heart and is the primary basis of death worldwide over the span of the past few decades. It associates many risk factors in heart disease and a need of the time to get accurate, reliable, and sensible approaches to make an early diagnosis to achieve prompt management of the disease. Data mining is a commonly used technique for processing enormous data in the healthcare domain. Researchers apply several data mining and machine learning techniques to analyse huge complex medical data, helping healthcare professionals to predict heart disease. This research paper presents various attributes related to heart disease, and the model on basis of supervised learning algorithms as Naive Bayes, decision tree, K-nearest neighbor, and random forest algorithm. It uses the existing dataset from the Cleveland database of UCI repository of heart disease patients. The dataset comprises 303 instances and 76 attributes. Of these 76 attributes, only 14 attributes are considered for testing, important to substantiate the performance of different algorithms. This research paper aims to envision the probability of developing heart disease in the patients. The results portray that the highest accuracy score is achieved with K-nearest neighbor.

Heart disease has long been a major threat to human health, causing about one-third of all deaths each year. Therefore, there is a need for accurate and effective prediction of cardiac disease using machine learning techniques. This paper uses data from 1,319 patients with heart disease and applies several machine-learning methods to study the relationship between a total of eight factors. The algorithms used in this paper include neural networks, decision trees, random forests, and so on. The best model was established by the random forest method, with an accuracy of 96.97%, precision of 97.03%, recall of 96.97%, and f1-Score of 0.9. It was found that troponin and CK-MB indicators had the highest influence weights, and the sum of the weights of each model accounted for more than 75% of the total weight, which demonstrated their significance in the prediction of heart disease, and the results could be utilized for the future prediction of heart disease. In addition, it also plays an essential role in the prediction of heart disease. To sum up, this is really important for the prediction of future heart disease.

Heart disease is one of the major causes of life complicacies and subsequently leading to death. The heart disease diagnosis and treatment are very complex, especially in the developing countries, due to the rare availability of efficient diagnostic tools and shortage of medical professionals and other resources which affect proper prediction and treatment of patients. Inadequate preventive measures, lack of experienced or unskilled medical professionals in the field are the leading contributing factors. Although, large proportion of heart diseases is preventable but they continue to rise mainly because preventive measures are inadequate. In today’s digital world, several clinical decision support systems on heart disease prediction have been developed by different scholars to simplify and ensure efficient diagnosis. This paper investigates the state of the art of various clinical decision support systems for heart disease prediction, proposed by various researchers using data mining and machine learning techniques. Classification algorithms such as the Naïve Bayes (NB), Decision Tree (DT), and Artificial Neural Network (ANN) have been widely employed to predict heart diseases, where various accuracies were obtained. Hence, only a marginal success is achieved in the creation of such predictive models for heart disease patients therefore, there is need for more complex models that incorporate multiple geographically diverse data sources to increase the accuracy of predicting the early onset of the disease.

Association of Metabolic Syndrome With Proximal and Synchronous Colorectal Neoplasm

Many factors contribute to the development of clinical atherosclerotic disease in adults. Observational studies conducted from the late 1940s onward have helped us to identify cigarette smoking, cholesterol levels, diabetes, blood pressure, and other factors as important determinants of cardiovascular disease (CVD) outcomes.1,2 The effects are multiplicative. For example, smoking, hypertension, and diabetes each double the risk of a heart attack, and having all 3 factors can lead to a risk that was ≈8 times that of an adult of similar age and sex who did not have any of the factors.3 A major focus in the earlier population and clinical studies was identification of persons at increased risk before the clinical atherosclerotic event. Less attention has been directed toward other types of events, such as occurrence of a myocardial infarction in persons presenting with chest pain, recurrent CVD events, and long-term vascular disease complications such as cardiac failure. The determinants of these vascular events are likely to include some of the classical risk factors, but new factors will no doubt be important as well. Improvements in outpatient and inpatient care, diagnosis, and biomarker discovery have reshaped the landscape. It is possible to visualize subclinical CVD with modern atherosclerotic imaging techniques such as carotid intima-media thickness and arterial calcification assessments. Clinical chemists and geneticists identify new biomarkers that promise to help refine assessments of CVD risk, but it is not clear how such tests should be used for research or for clinical care. This article addresses several of the questions posed above and takes the perspective of research and care for clinical atherosclerotic disease, emphasizing the progress we have made from traditional risk factor assessment to the present time that includes newer assessment methods such as genomics, metabolomics, and predictive medicine. ### Traditional Risk Factors for Cardiovascular Disease The traditional risk factors include cholesterol, blood …

DOI: http://dx.doi.org/10.3126/hprospect.v11i0.7420Health Prospect 2012 Vol.11

The role of classical risk factors, particularly high blood pressure, high blood cholesterol levels, and cigarette smoking in predicting incidence of heart disease has been well documented. However, information about the predictive value of these risk factors in persons who already have heart disease is largely derived from selected hospital or case series with limited time of follow-up and predominantly involving men. We examined the role of risk factors in predicting subsequent all-cause and cardiovascular disease mortality in a population of men and women, age 50 to 79 years (mean age, 66 yr), who reported a history of heart disease at baseline examination and who have been followed for nine years. One hundred ninety-one men and 96 women reported a personal history of heart attack or heart failure. In these men, age, high systolic blood pressure, and family history of heart attack were significant independent predictors of mortality attributable to cardiovascular disease and all causes. In contrast, only current cigarette smoking habit and personal history of diabetes significantly predicted increased mortality in these women. High systolic blood pressure in men and cigarette smoking in women appear to be the two major potentially remediable risk factors for mortality in older subjects with a history of heart disease.

Cardiovascular disease remains the leading cause of death in the USA and is associated with several modifiable (hypertension, diabetes, high cholesterol, tobacco use, physical inactivity, obesity and unhealthy diet) and nonmodifiable (age, gender and family history) risk factors. The role of psychosocial risk factors in the development of cardiovascular disease has a growing body of literature, and differences in men and women have been identified. The Women's Ischemia Syndrome Evaluation provides insight into psychosocial risk factors in a cohort of women presenting with chest pain who had a comprehensive battery of psychosocial assessments and long-term follow-up. This review focuses on symptom presentation for chest pain and its relationship to cardiovascular disease morbidity and mortality, quality of life, healthcare costs and psychosocial predictor variables, including anxiety, depression, hostility and social networks. In the Women's Ischemia Syndrome Evaluation, persistent chest pain was associated with an increased rate of adverse events and relatively high rates of depression and anxiety, with reduced functional capacity and impaired quality of life, over a median of 6 years of follow-up. More research is needed to better understand the relationships between symptoms and negative emotions and to determine whether psychological (pharmacologic and/or cognitive) interventions might impact both psychological and cardiovascular outcomes.

ObjectivesTo identify and quantify major external (non-genetic) factors that contribute to death in Chaoyang District of Beijing, China in 2007.MethodsThe death registration data reported to the Center of Disease Control...