Prediction of Heart Disease using an Ensemble Learning Approach

Abstract

The ability to predict diseases early is essential for improving healthcare quality and can assist patients in avoiding potentially dangerous health conditions before it is too late. Various machine learning techniques are used in the medical field. Nonetheless, machine learning is critical in determining the future of pharmaceuticals and patients’ health. This is because the various classification techniques provide a high level of accuracy. However, because so much data are being gathered from patients, it becomes harder to find meaningful cardiac disease predictions. A vital research task is to identify these characteristics. Individual classification algorithms in this situation cannot generate flawless models capable of reliably predicting heart disease. As a result, higher performance might be achieved by using ensemble learning approaches (ELA), producing accurate cardiac disease predictions. In the present research work, we utilized an ELA for the early prediction of heart disease, using a new combination including four machine learning algorithms—adaptive boosting, support vector machine, decision tree, and random forest—to increase the accuracy of the prediction results. We used two wrapper methods for feature selection: forward selection and backward elimination. We used the proposed model with three datasets: the StatLog UCI dataset, the Z-Alizadeh Sani dataset, and the Cardiovascular Disease (CVD) dataset. We obtained the highest accuracy when using our proposed model with the Z-Alizadeh Sani dataset, where it was 0.91, while the StatLog UCI dataset was 0.83. The CVD dataset obtained the lowest accuracy, 0.73.