Analysis and comparison of Naive Bayes algorithm for prediction of cardiovascular disease over support vector machine algorithm with improved precision

Cardiometry

article

Rajvardhan Gadde¹  Neelam Sanjeev Kumar¹ 

[1] Department of Biomedical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University

关键词: Cardiovascular Disease Detection;    machine learning;    Naive Bayes algorithm;    Support Vector Machine algorithm;    Accuracy;    Precision;   

DOI  :  10.18137/cardiometry.2022.25.963969

学科分类：环境科学（综合）

来源: Russian New University

【 摘 要 】

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.

【 授权许可】

CC BY