Abstract

When the coronary veins become hardened and narrow, they cause Coronary Artery Disease (CAD), which restricts blood flow to the heart muscles. It has the greatest mortality rate of any cardiac disease and is the most prevalent. Early detection of CAD can facilitate treatment and prevent the illness from developing. In addition to early CAD discovery, optimal treatment can enhance these patients' prognoses. This paper suggests a comprehensive preprocessing method to forecast Coronary Artery Disease (CAD). Replacement of null values, feature selection, and prediction are all components of the method. This work aims to predict the risk of CAD using the CNN-LSTM(Convolutional Neural Network-Long Short-Term Memory) approach. CNN has carried out the data cleansing, and feature selection, and LSTM does the prediction of CAD.