Estimation of accuracy rate in predicting cardiovascular disease using Gaussian Naive Bayes algorithm with logistic regression

Talluri Vishnuvardhan¹, A.Rama¹

¹ Department of Computer Science and Engineering,Saveetha School of Engineering,Saveetha Institute of Medical and Technical Sciences,Saveetha University, Chennai, TamilNadu, India

Abstract

Aim: Comparison of accuracy rate in prediction of cardiovascular disease using Naive Bayes with Logistic Regression. Materials and Methods: The Naive Bayes (N=10) and Logistic Regression Algorithm (N=10) these two algorithms are calculated by using 2 Groups and taken 20 samples for both algorithm and accuracy in this work. The sample size is determined using the G power Calculator and it’s found to be 10. Results: Based on the Results Accuracy obtained in terms of accuracy is identified by Naive Bayes (87.02%) over the Logistic Regression algorithm (92.18%). Statistical significance difference between novel Naive Bayes algorithm and Logistic Regression Algorithm was found to be p=0.001 (2 tailed) (p<0.05). Conclusion: Prediction of cardiovascular disease using Logistic Regression is significantly better than the Naive Bayes.

Imprint

Talluri Vishnuvardhan, A.Rama. Estimation of Accuracy Rate in Predicting Cardiovascular Disease using Gaussian Naive Bayes Algorithm with Logistic Regression. Cardiometry; Issue 25; December 2022; p.1532-1537; DOI: 10.18137/cardiometry.2022.25.15321537; Available from: https://www.cardiometry.net/issues/no25-december-2022/estimation-accuracy-rate

Keywords

Novel naive bayes, Novel logistic regression, Machine learning, Healthcare, Blood Coronary, Vasculitis

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