Analysis and Comparison of Random Forest Algorithm for Prediction of Cardiovascular Disease over Support Vector Machine Algorithm with Improved Precision

Abstract

Aim: To find the best algorithm for the prediction of innovative cardiovascular disease accurately, with fewer errors between Random Forest and Support Vector Machine classifiers. Materials and Methods: Data collection containing various data points for predicting innovative cardiovascular disease from UCI machine learning repository. Classification is performed by Random Forest classifier (N=20) over Support Vector Machine (N=20) total sample size calculation is done through clinical. com. The accuracy was calculated using Matlab software and the outputs are graphed using SPSS software. Results: comparison of accuracy rate is done by independent sample test using SPSS software. There is a statistical indifference between Random Forest and Support Vector Machine. Support Vector Machine algorithm (87.38%) showed better results in comparison to Random Forest (83.50%). Conclusion: Support Vector Machine algorithm appears to give better accuracy than Random Forest for the prediction of innovative Cardiovascular Disease.