Comparing the efficiency of heart disease prediction using novel random forest, logistic regression and decision tree and SVM algorithms

Cardiometry

article

P. Prasanna Sai Teja¹  Veeramani T¹ 

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

关键词: Logistic Regression;    Decision Tree;    Random Forest;    Support Vector Machine;    Heart Disease Prediction;    Data Mining;   

DOI  :  10.18137/cardiometry.2022.25.14911499

学科分类：环境科学（综合）

来源: Russian New University

【 摘 要 】

Aim: The aim of the work is to evaluate the accuracy and precision in predicting heart disease using Support Vector Machine (SVM) , Random forest (RF), Logistic Regression (LR), Decision Tree (DT) Classification algorithms. Materials and Methods: Classification algorithm is appealed on a heart dataset which consists of 180 records. A framework for heart disease prediction in the medical sector comparing Random forest, Logistic Regression , Decision Tree and SVM classifiers has been proposed and developed. The sample size was calculated as 55 in each group using G power 80%. Sample size was calculated using clincalc analysis, with alpha and beta values 0.05 and 0.5, 95% confidence, pretest power 80% and enrolment ratio 1. Results: The Novel Random Forest Algorithm (92.13%) , Support Vector Machine (62.51%) , Logistic Regression (84.89%), Decision Tree (86.25%) classifiers produce respectively. SVM, RF exists a statistically significant difference between the two groups (p=0.001,p=.004;p0.05) both with confidence interval 95%. Hence Random forest is better than SVM, RF, DT classifiers. Conclusion: The results show that the performance of RF is better when compared with SVM, LR and DT in terms of both precision and accuracy.

【 授权许可】

CC BY