Analysis and Comparison of Neural Network 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 Neural Network 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 Neural Network 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 Neural Network and Support Vector Machine. Support Vector Machine algorithm (87.38%) showed better results in comparison to Neural Network (81.12%). Conclusion: Support Vector Machine algorithm appears to give better accuracy than Neural Network for the prediction of Innovative Cardiovascular Disease.

Imprint

Rajvardhan Gadde, Neelam Sanjeev Kumar. Analysis and Comparison of Neural Network Algorithm for Prediction of Cardiovascular Disease over Support Vector Machine Algorithm with Improved Precision. Cardiometry; Issue 25; December 2022; p.970-976; DOI: 10.18137/cardiometry.2022.25.970976; Available from: https://www.cardiometry.net/issues/no25-december-2022/neural-network-algorithm