Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers

Abstract

Coronary heart disease is one of the major causes of deaths around the globe. Predicating a heart disease is one of the most challenging tasks in the field of clinical data analysis. Machine learning (ML) is useful in diagnostic assistance in terms of decision making and prediction on the basis of the data produced by healthcare sector globally. We have also perceived ML techniques employed in the medical field of disease prediction. In this regard, numerous research studies have been shown on heart disease prediction using an ML classifier. In this paper, we used eleven ML classifiers to identify key features, which improved the predictability of heart disease. To introduce the prediction model, various feature combinations and well-known classification algorithms were used. We achieved 95% accuracy with gradient boosted trees and multilayer perceptron in the heart disease prediction model. The Random Forest gives a better performance level in heart disease prediction, with an accuracy level of 96%.

Keywords: disease prediction; heart disease dataset; machine learning; supervised learning.

Figure 1

System Working Methodology.

Figure 2

Correlation Matrix with a Heatmap.

Figure 3

Heart Disease Status.

Figure 4

Sex heart disease chances.

Figure 5

Heart Disease Dataset Age Statistics.

Figure 6

Chest pain vs. heart disease chances.

Figure 7

Fasting blood sugar vs. disease chances.

Figure 8

Resting ECG vs. heart disease chances.

Figure 9

Exercise-Induced Angina vs. disease chances.

Figure 10

Slope vs. heart disease chances.

Figure 11

Coronary Artery vs. disease chances.

Figure 12

Thalassemia vs. heart disease chances.

Figure 13

ML Classifiers Accuracy.

Figure 14

Random Forest Classifiers ROC.

Figure 15

Gradient Boosting Tree Classifiers ROC.

Figure 16

Multilayer perceptron Classifiers ROC.