Artificial Intelligence-Based Ensemble Model for Rapid Prediction of Heart Disease

Heart disease is the leading cause of mortality among men and women. Accurate and rapid diagnosis of heart disease will assist in saving many lives. To develop a novel ensemble framework based on heterogeneous classifiers namely support vector machine (SVM), Naïve Bayes (NB), and artificial neural networks (ANN) for rapid prediction of heart disease. The present study also verifies the most accurate algorithm among all three. Data are collected from the UCI machine learning repository. After pre-processing, the data were divided into training and test data in a ratio of 80:20. Using the training data, the three contributing algorithms were trained by providing heart disease status. The algorithms were tested with the unseen data instances and hence evaluated for accuracy. The ensemble technique uses the results from individual classifiers and yields a result based on majority voting method. The ensemble model was observed to predict heart disease with an accuracy of 87.05% followed by ANN (84.74%), NB (81.35%) and SVM (79.66%). Among the individual classifiers, ANN had the least miss-classification rate and performed best in terms of all other model diagnostics. The use of the proposed ensemble classifier is recommended to predict the heart condition to have better accuracy and least miss-classification. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.