Coronary Heart Disease Risk Prediction Using Combined Expert System and Deep Learning Methods

Abstract

Coronary Heart Disease (CHD) encompasses several conditions such as chest pain, heart attack, and cardiac arrest. It is a major form of cardiovascular disease and a leading cause of death. CHD has become increasingly common and occurs when plaque builds up along the inner walls of the heart’s arteries, restricting normal blood flow. This study presents a web-based expert system designed to estimate an individual’s likelihood of developing CHD based on key risk factors such as cholesterol level, diabetes status, and smoking habits. The system integrates two artificial intelligence techniques: a rule-based expert system and a deep learning model. The rule-based component is implemented using CLIPS, while the deep learning model is developed using the TensorFlow framework to train and evaluate the dataset. The primary objective of this project is to assess the risk of heart disease early, enabling individuals to take preventive measures before serious complications occur.