Predicting the Analysis of Heart Disease Symptoms Using Medicinal Data Mining Methods

Abstract—Predicting the Analysis of Heart Disease Symptoms Using Medicinal Data Mining Methods. Heart disease (HD) is a major cause of morbidity and mortality in the modern society. Medical diagnosis is extremely important but complicated task that should be performed accurately and efficiently. This study analyzes the Behavioral Risk Factor Surveillance System, survey to test whether self-reported cardiovascular disease rates are higher in Singareni coal mining regions in Andhra Pradesh state, India, compared to other regions after control for other risks. Dependent variables include self-reported measures of being diagnosed with cardiovascular disease (CVD) or with a specific form of CVD including (1) chest pain (2) stroke and (3) heart attack. Heart care study specifies 15 attributes to predict the morbidity. Beside regular attributes other general attributes BMI (Body Mass Index), physician supply, age, ethnicity, < Final Year Projects > education, income, and others are used for prediction. An automated system for medical diagnosis would enhance medical care and reduce costs. In this paper popular data mining techniques namely, Decision Trees, Naïve Bayes and Neural Network are used for prediction of heart disease.