Prediction of Coronary Heart Disease using Machine Learning

TLDR

Medical data analysis offers rich information, and coronary heart disease is a leading global cause of death, making early detection crucial, yet predicting CHD is difficult due to complex data and correlations. This study aims to predict coronary heart disease from historical medical data using machine learning techniques. The authors trained Naïve Bayes, Support Vector Machine, and Decision Tree models on the 462‑instance South African Heart Disease dataset, employing 10‑fold cross‑validation to evaluate performance. Results indicate that Naïve Bayes probabilistic models show promise in detecting coronary heart disease.

Abstract

The field of medical analysis is often referred to be a valuable source of rich information. Coronary Heart Disease (CHD) is one of the major causes of death all around the world therefore early detection of CHD can help reduce these rates. The challenge lies in the complexity of the data and correlations when it comes to prediction using conventional techniques. The aim of this research is to use the historical medical data to predict CHD using Machine Learning (ML) technology. The scope of this research is limited to using three supervised learning techniques namely Naïve Bayes (NB), Support Vector Machine (SVM) and Decision Tree (DT), to discover correlations in CHD data that might help improving the prediction rate. Using the South African Heart Disease dataset of 462 instances, intelligent models are derived by the considered ML techniques using 10-fold cross validation. Empirical results using different performance evaluation measures report that probabilistic models derived by NB are promising in detecting CHD.