Efficient Prediction of Stroke Patients Using Random Forest Algorithm in Comparison to Support Vector Machine

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.