Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 10.54216/JAIM https://www.americaspg.com/journals/show/1878 2022 2022 Decision Support Department, Faculty of Computers and Informatics Zagazig University, Zagazig, 44519, Egypt Amal F. Abdel Abdel-Gawad Decision Support Department, Faculty of Computers and Informatics Zagazig University, Zagazig, 44519, Egypt Salwa El El-Sayed Decision Support Department, Faculty of Computers and Informatics Zagazig University, Zagazig, 44519, Egypt Mahmoud M. Ismail   Stroke is a leading cause of disability and mortality worldwide, emphasizing the need for accurate and timely prediction methods. In recent years, advancements in machine learning and computational healthcare have shown promising results in various medical domains. This paper presents a comprehensive study on the application of machine learning techniques for stroke prediction in computational healthcare. The objective of this research is to develop a robust and accurate stroke prediction model that can assist healthcare professionals in identifying individuals at high risk of stroke. Leveraging a diverse dataset consisting of demographic information, medical history, and clinical measurements, a range of machine learning algorithms is employed to extract meaningful patterns and relationships. Feature selection techniques are utilized to identify the most relevant predictors, ensuring optimal model performance. Through rigorous experimentation and evaluation, the proposed machine learning model demonstrates superior performance in stroke prediction compared to traditional risk assessment methods. The implications of this research extend beyond stroke prediction, with the proposed methods serving as a foundation for the development of similar predictive models in other healthcare domains. 2023 2023 51 59 10.54216/JAIM.030105 https://www.americaspg.com/articleinfo/28/show/1878