Classification and prediction of heart disease using novel random forest algorithm by comparing logistic regression for obtaining better accuracy

Cardiometry

article

T Poojitha¹  Mahaveerakannan R¹ 

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

关键词: Coronary Disease;    Novel Random Forest Algorithm;    Logistic Regression;    Machine Learning;    Classification;    Prediction;   

DOI  :  10.18137/cardiometry.2022.25.15381545

学科分类：环境科学（综合）

来源: Russian New University

【 摘 要 】

Aim: Heart attacks are usually caused due to blockages, partially or completely, of the heart’s veins or arteries that constrict the flow of blood from or to the heart. The primary objective of this review aims to be seen as the most appropriate algorithm to give us the ideal prediction. We will be comparing the novel Random forest with Logistic regression to find out which of these can give us the best accuracy. Material and Methods: The study used 143 samples with novel Random Forest and Logistic Regression is executed with varying training and testing splits for foreseeing the accuracy of coronary disease prediction with the 80% of G-power value and heart disease data were gathered from multiple web sources, including latest The study's findings and criterion were 0.05%, with a 95% probability value, average, and confidence interval. The performance accuracy rate of the classifiers is used to evaluate the coronary disease dataset. There was a statistically significant value test between the novel Random Forest and Logistic Regression is 0.046 (p<0.05). Results and Discussion: The accuracy of predicting coronary disease in the novel Random Forest 90.16 % and Logistic Regression 85.25 % is obtained. Conclusion: This study concludes that the Prediction of Coronary disease using the novel Random Forest (RF) algorithm looks to be fundamentally superior to the Logistic Regression (LR) with increased precision.

【 授权许可】

CC BY