Heart disease prediction based on age detection using logistic regression over random forest

Cardiometry

article

C.B.M Karthi¹  A. Kalaivani¹ 

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

关键词: Logistic Regression;    Novel Random Forest;    Heart disease prediction;    Accuracy;    Machine Learning;   

DOI  :  10.18137/cardiometry.2022.25.17311737

学科分类：环境科学（综合）

来源: Russian New University

【 摘 要 】

Aim: To improve the accuracy in Heart Disease Prediction using Logistic Regression and Random Forest. Materials and Methods: This study contains 2 groups i.e Logistic Regression and Random Forest. Each group consists of a sample size of 10 and the study parameters include alpha value 0.01, beta value 0.2, and the Gpower value of 0.8. Results: The Logistic Regression achieved improved accuracy of 91.60 then the Random Forest in Heart Disease Prediction. The statistical significance difference is 0.01 (p<0.05). Conclusion: The Logistic Regression model is significantly better than the Random Forest in Heart Disease Prediction. It can be also considered a better option for Heart Disease Prediction.

【 授权许可】

CC BY