Prediction of heart disease using decision tree over logistic regression using machine learning with improved accuracy

Cardiometry

article

K N S Shanmukha Raj¹  K Thinakaran¹ 

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

关键词: Decision Tree;    Logistic Regression;    Principal Component Analysis;    Machine Learning;    Supervised Classification;    Novel Dimensionality Reduction;   

DOI  :  10.18137/cardiometry.2022.25.15141519

学科分类：环境科学（综合）

来源: Russian New University

【 摘 要 】

Aim: Predicting heart disease using the Decision Tree and comparing its feature extraction precision with the Logistic Regression algorithm for improving the accuracy of the prediction. Methods and Materials: In the proposed work, predicting heart disease was carried out using machine learning algorithms such as Logistic Regression (n=10) and Decision tree (n=10). Here the pretest power analysis was carried out with 80% and the sample size for the two groups are 20. Results: From the implemented experiment, the Decision Tree accuracy significantly better than the Logistic Regression 80.10%. There is a measurable 2-tailed huge distinction in accuracy for two algorithms is 0.001 (p<0.05) Conclusion: The Decision Tree algorithm got better accuracy than Logistic Regression for Predicting heart disease.

【 授权许可】

CC BY