Effect of Imbalance Data Handling Techniques to Improve the Accuracy of Heart Disease Prediction using Machine Learning and Deep Learning

Heart disease is the prominent life-threating cause of death. Early stage of prediction can reduce the death percentage. This paper investigates the effect of different imbalance data handling techniques on the accuracy to predict the heart disease using machine learning, deep learning methods and an ensemble method. Most of the algorithms show better accuracy in balanced data instead of imbalance data. Support Vector Machine, Multilayer Perceptron, ensemble of Logistic Regression and Multilayer Perceptron shows 96% accuracy on balanced data using SMOTETomek hybrid balancing techniques. Accuracy, Precision, Recall, F-1 score, Specificity, Cohen Kappa, AUC score and ROC curve is used to measure the performance of each algorithm.