Improving Heart Disease Diagnosis: An Ensemble Machine Learning Approach

Improving the performance of machine learning approaches in the field of health is of the utmost importance because early and correct diagnosis and treatment of diseases are essential for human life. From this point of view, an ensemble machine learning approach has been proposed for the diagnosis of heart disease within the scope of this study. In the first step of the proposed approach, feature extraction is performed using the Convolutional Neural Network on the dataset. In the next step, prediction results are obtained using individual classification methods such as Multi-layer Perceptron, Support Vector Machine, and Random Forest. Finally, the obtained prediction results are combined using the majority voting method. The results which are compared according to the critical classification performance criteria show that the proposed ensemble method gives better results than the individual methods. Heart disease can be predicted with an accuracy of 86.4% with the proposed ensemble approach.