Coronary Heart Disease (CHD) Risk Prediction Using Decision Tree (DT) And Multi Layer Perceptron (MLP)

Abstract

It is an important and complex task in medicine to determine the risk of Coronary Heart Disease (CHD) based on lifestyle factors. By merging Decision Tree (DT) and Multi Layer Perceptron (MLP), this work aims to develop a speedy and automatic prediction of CHD risk (MLP). Analysis/Methods of Risk identification is carried out in the first stage and level prediction is carried out in the second stage, according to the hybrid technique. A C4.5 algorithm is used in DT's initial step to classify physical and biological components This is followed by an MLP analysis of the DT-identified CHD risk cases, which includes habitation and medical history attributes Findings: Based on its classification results, DT and MLP are both classified with an accuracy rate of 96.67%. The sensitivity and specificity of the suggested technique are used in the performance analysis, which helps to decrease healthcare expenses, further invasive CHD risk examinations, and patient waiting times. This paper proposes an ideal predictive technique for diagnosing CHD risk, which might serve as a blueprint for physicians in clinical diagnosis.