Comparison of accuracy rate in prediction of cardiovascular disease using random forest 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 Novel Random Forest with Logistic Regression. Materials and Methods: The Novel Random forest (N=20) and Novel Logistic Regression Algorithm (N=20) 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: The Random Forest exhibited 89.06% accuracy whilst a Logistic Regression has shown 92.18%. accuracy. Statistical significance difference between Random forest algorithm and Novel Logistic Regression Algorithm was found to be p=0.001 (2 tailed) (p<0.5). Conclusion: Prediction of cardiovascular disease using Logistic Regression is significantly better than the Random Forest.

Imprint

Talluri Vishnuvardhan, A.Rama. Comparison of Accuracy Rate in Prediction of Cardiovascular Disease using Random Forest with Logistic Regression. Cardiometry; Issue 25; December 2022; p.1526-1531; DOI: 10.18137/cardiometry.2022.25.15261531; Available from: https://www.cardiometry.net/issues/no25-december-2022/rate-prediction-cardiovascular-disease

Keywords

Prediction of cardiovascular disease, Novel Random forest, Novel Logistic Regression, Smoking, Endocytosis, Hyperglycemia

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