Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3458 2018 2018 Faculty of Information Technology, Department of Software Engineering, Philadelphia University, Amman, Jordan Nour Nour Cybersecurity Department, College of Computer Sciences and Informatics, Amman Arab University, Amman, Jordan Qusay Bsoul Cybersecurity Department, Faculty of Science and Information Technology, Jadara University, Irbid, Jordan Firas Zawaideh Department of Clinical Pharmacy Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, Jordan Ala Alzoubi Faculty of Engineering, Misr International University, cairo , Egypt Silvyras Sayed MEU Research Unit, Middle East University, Amman, Jordan Raghad W. Bsoul Jadara Research Center, Jadara University, Irbid, Jordan; College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait Diaa Salama AbdElminaam College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait Nour Mostafa In this article, we use machine learning approaches to give a thorough investigation into the prediction of cardiac illnesses and strokes. The Stroke Prediction Dataset and the Heart Failure Prediction Dataset are the two datasets that we use. Our objective is to maximize accuracy and minimize Mean Absolute Error (MAE) and Mean Squared Error (MSE) in order to enhance predictive performance. We use a variety of machine learning methods, such as Random Forests, Naive Bayes, Decision Trees, and k-Nearest Neighbors (KNN). We also use Artificial Neural Networks (ANN) and Multi-Layer Perceptrons (MLP) as deep learning models. We use oversampling approaches to rectify the imbalance in classes. For hyperparameter tweaking, we also use Grid Search and k-Fold Cross Validation. Our goal is to deliver valuable insights into early detection and preventive measures through comprehensive testing and assessment for prevention of strokes and heart diseases. 2025 2025 182 203 10.54216/FPA.180113 https://www.americaspg.com/articleinfo/3/show/3458