Efficient Prediction of Stroke Patients Using Random Forest Algorithm in Comparison to Support Vector Machine Ritaban Mitra a and T. Rajendran b,1 a Research Scholar, Dept. Of CSE, Saveetha School of Engineering, b Asso. Prof., Dept. Of CSE, Saveetha School of Engineering, a,b SIMATS, Chennai.Tamil Nadu, India Abstract. The work aims to make an efficient prediction of stroke in patients using several Machine learning modeling techniques and evaluating their performance. The two groups used in this paper are the Random Forest Algorithm (RFA) and the Support Vector Machine(SVM) Algorithm. The dataset implemented and tested consists of over 5000 records of patients' medical and personal records. They were using N = 20 iterations for each algorithm. The G-Power test used is about 80%. The results of our work have given us the mean accuracy of 94.61 on Random Forest and 93.91 on Support Vector Machine Algorithms. The statistically significant difference was obtained by generating independent sample t-tests at 0.015. This work is intended to implement innovative approaches to increase the efficiency of stroke prediction algorithms and improve the accuracy of existing algorithms. The results show that the Random Forest Model performs higher than Support Vector Machines. Keywords. Innovative Stroke Prediction, Machine learning, Data Science, Random Forest Algorithm, Support Vector Machine Algorithm, Statistical Analysis. 1. Introduction Stroke is the second biggest reason of mortality globally, as per the WHO report, accounting for 11% of fatalities yearly. A stroke is a medical emergency that causes damage to the brain due to a shortage of blood supply, causing brain cells to die. This research paper will explore stroke conditions and use a Machine learning approach to solve this problem and develop an Innovative Stroke Prediction technique in patients [1]. Over the years, as computers have become more powerful, their ability to support research work in the medical domain has also increased. This is a massive benefit to the world as it can combine the power of human intelligence with the potential of computers and gain insights into patterns from Statistical Analysis [2]. This analysis is done using a Data Science driven approach [3]. Applications of the research include clinical prognosis and drug development [2]. The Prediction and classification of heart failure have been made using a Machine learning approach [4]. In paper [5], a 1 T. Rajendran , Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India, Email:rajendrant.sse@saveetha.com. Advances in Parallel Computing Algorithms, Tools and Paradigms D.J. Hemanth et al. (Eds.) © 2022 The authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). doi:10.3233/APC220075 530