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.

Imprint

Rajvardhan Gadde, Neelam Sanjeev Kumar. Analysis and Comparison of Random Forest Algorithm for Prediction of Cardiovascular Disease over Support Vector Machine Algorithm with Improved Precision . Cardiometry; Issue 25; December 2022; p.977-982; DOI: 10.18137/cardiometry.2022.25.977982; Available from: https://www.cardiometry.net/issues/no25-december-2022/random-forest-algorithm