Abstract
According to the world health organization (WHO), cardiovascular diseases are the leading cause of death worldwide, and identifying those at risk can help prevent sudden deaths. Some computational methods have been proposed to predict the patient’s heart disease risk. Meanwhile, the accurate prediction of diseases is a critical aspect of machine learning and to further enhance the classification of heart disease risk, this paper proposes an improved ensemble learning approach. The mean of the data columns is used to partition the dataset into smaller subsets, and then a classification and regression tree (CART) algorithm is applied to model each partition. Randomization is introduced during the data partitioning. The resulting ensemble produces a robust model for the prediction of heart disease risk. To evaluate the effectiveness of the proposed method, the Cleveland and Framingham heart disease datasets are used. When compared with other machine learning methods and some recent scholarly works, the proposed method showed significant improvement.