Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 10.54216/JAIM https://www.americaspg.com/journals/show/1938 2022 2022 Energies and Materials Research Laboratory, Faculty of Sciences and Technology, University of Tamanghasset, Tamanrasset, 10034, Algeria;Sustainable Development and Computer Science Laboratory, Faculty of Sciences and Technology, Ahmed Draia University of Adrar, Adrar, Algeria Nadjem Bailek Electronics and Communications Engineering Department, Faculty of Engineering, Delta University for Science and Technology, Gamasa City 11152, Egypt Mohamed Saber Smart healthcare systems rely heavily on disease prediction because it paves the way for early detection and prompt action, both of which enhance patient outcomes. In this research, we present a machine learning (ML) method for identifying data patterns that might be used to foretell the occurrence of cardiac disease. Our approach entails cleaning the data used for predicting cardiac issues and then using a Support Vector Machine (SVM). Age, sex, chest pain type, blood pressure, cholesterol, and exercise-induced angina are only few of the attributes included in the dataset. Insights into the distributional analysis of categorical and numeric variables, as well as potential connections and trends, are gained through exploratory data analysis (EDA). Cross-validation results show that the SVM model performs exceptionally well, with higher accuracy and AUC than competing models. By utilizing ML methods, our research aids in the development of intelligent healthcare systems. These results add to our understanding of how to forecast diseases and show how machine learning may transform healthcare systems to improve patient outcomes. 2023 2023 48 55 10.54216/JAIM.030205 https://www.americaspg.com/articleinfo/28/show/1938