Analysis and Comparison of Naive Bayes 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 Novel Cardiovascular Disease Detection accurately, with fewer errors between Novel Naive Bayes and Support Vector Machine classifiers. Materials and Methods: Data collection containing various data points for predicting Novel Cardiovascular Disease Detection from UCI machine learning repository. Classification is performed by Naive Bayes 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 the Naive Bayes algorithm and Support Vector Machine algorithm. Support Vector Machine algorithm (87.38%) showed better results in comparison to Novel Naive Bayes algorithm (75.13%). Conclusion: Support Vector Machine algorithm appears to give better accuracy than Naive Bayes algorithm for the prediction of Novel Cardiovascular Disease Detection.