Prediction of heart disease using forest algorithm over decision tree 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: Machine Learning;    Forest Algorithm;    Prediction of Heart Disease;    Supervised Classification;    Novel Principal Component Analysis;    Decision Tree;   

DOI  :  10.18137/cardiometry.2022.25.15201525

学科分类：环境科学（综合）

来源: Russian New University

【 摘 要 】

Aim: To predict the heart disease using Forest Algorithm and comparing it with Decision Tree algorithm for improving the accuracy in predicting heart disease. Methods and Materials: Anticipating coronary illness expectation was completed utilising machine learning calculations, for example, Forest Algorithm and Decision tree. Here the pretest power analysis was carried out with 80% and the sample size for the two groups are 20. Results: Forest Algorithm accuracy is 90.00% while the Decision Tree algorithm has shown an accuracy of 85.00%. There is a measurable 2-tailed significant distinction in exactness for two calculations is 0.001 (p<0.05) by performing independent samples T-tests. Conclusion: The Forest Algorithm accuracy is more significant and more accurate than the Decision Tree for predicting heart disease.

【 授权许可】

CC BY