Prediction of Heart Diseases by Using Supervised Machine Learning Algorithms

Abstract

Heart Disease is a complex and life-threatening ailment that poses a significant mortality risk around the world, with nearly a third of global deaths attributable to heart-related conditions. The early prediction and detection of heart disease are of utmost importance in the medical field, as they may lead to saving numerous lives. However, the lack of heart expertise in many countries and the high rates of misdiagnosis highlight the need for accurate and efficient prediction methods. Machine learning-based approaches have the potential to address this need, particularly in handling the large amounts of data generated by medical sectors and hospitals. In this study, the performance and accuracy of several supervised machine learning algorithms were compared for heart disease prediction using a dataset obtained from PhysioNet databases. The classifiers that were applied included Artificial Neural Network (ANN), Gradient Boosting, Decision Tree, Naive Bayes, and Random Forest. Results showed that the ANN algorithm achieved the highest Accuracy of 94.1%, with a sensitivity and specificity of 94.1%. The study thus concluded that supervised machine learning techniques can be utilized with great success to forecast heart disease, displaying exceptional potential for practical application and accuracy