Machine-Learning-Based Prediction Models of Coronary Heart Disease Using Naïve Bayes and Random Forest Algorithms

Coronary heart disease (CHD), alternatively known as cardiovascular disease (CVD) is the number one cause of death in the world. Accordingly, a plethora of research have been conducted to predict the early diagnosis of the heart disease and determine the most important risk factors associated with the disease. Despite these considerable efforts, the accuracy of the prediction has remained inadequate and the most important risk factors have remained elusive. This research paper discusses many risk factors associated with the disease and presents the prediction models of coronary heart disease using supervised machine learning algorithms, namely Gaussian Naïve Bayes, Bernoulli Naïve Bayes and Random Forest algorithms. It uses the public dataset from the Cleveland database of UCI repository of coronary heart disease patients. The results show that the Gaussian Naïve Bayes, Bernoulli Naïve Bayes and Random Forest algorithms have accuracies of 85.00%, 85.00% and 75.00%, respectively. Moreover, the precision, F-measure and recall of the Gaussian and Bernoulli Naïve Bayes are higher than those of Random Forest algorithm, signifying its importance in predicting the early diagnosis of the disease.