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.