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.

Imprint

Rajvardhan Gadde, Neelam Sanjeev Kumar. Analysis and Comparison of Naive Bayes Algorithm for Prediction of Cardiovascular Disease over Support Vector Machine Algorithm with Improved Precision . Cardiometry; Issue 25; December 2022; p.963-969; DOI: 10.18137/cardiometry.2022.25.963969; Available from: https://www.cardiometry.net/issues/no25-december-2022/naive-bayes-algorithm