Chronic Heart Disease Prediction Using Data Mining Techniques

Abstract

Cardiovascular disease has become one of the most widespread diseases in the world at present. It is estimated to have caused around 17.9 million deaths in 2017 which constitutes about 15% of all natural deaths. One major type of cardiovascular disease is chronic heart disease. CHD can be detected at the initial stages by measuring the levels of various health parameters like blood pressure, cholesterol level, heart rate and glucose level. Other characteristics of a person like number of cigarettes smoked per day and BMI level also help to diagnose CHD. This paper focuses on utilizing data mining techniques to predict whether a person is suffering from CHD based on data about various symptoms of CHD. This paper proposes two prediction models namely XGBoost algorithm and logistic regression approach to predict the CHD values. This prediction of CHD at its early stage will help in reducing the risk of CHD on a person.