aspg-31-1845-1791686456 20261011024056 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Advances in Applied Computational Intelligence IJAACI 2833-5600 2022 1 2 Heba R. Abdelhady Decision Support Department, Faculty of Computers and Informatics Zagazig University, Zagazig, 44519, Egypt Mahmoud M. Ismail Decision Support Department, Faculty of Computers and Informatics Zagazig University, Zagazig, 44519, Egypt Providing medical treatment is a vital part of human existence. Diseases of the heart and blood arteries are often referred to as cardiovascular disease. Predicting cardiovascular illness early on allowed doctors to make adjustments for individuals at high risk, lowering their mortality rate. Machine learning techniques are necessary for making appropriate judgments in the forecasting of cardiac problems because of the vast amounts of medical data available in the healthcare business. Mixed machine-learning approaches are the subject of recent research on unifying these methods. The study proposed machine learning models to predict the heart disease. In order to determine whether or not a person has heart disease, this project presents a model for forecasting. To achieve this, we compare the accuracy of using rules to that of using the Support Vector Machine (SVM), Random forest (RF), and Decision Tree (DT) separately on the dataset. 2022 56 62 1845 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJAACI.010204 https://www.americaspg.com/journal/31/article/1845 https://www.americaspg.com/storage/11686323102.pdf