Prediction of Heart Disease using Forest Algorithm over Decision Tree using Machine Learning with Improved Accuracy

被引：0

作者：

Raj, K. N. S. Shanmukha ^{[1
]}

Thinakaran, K. ^{[1
]}

机构：

[1] Saveetha Univ, Saveetha Inst Med & Tech Sci, Saveetha Sch Engn, Dept Comp Sci & Engn, Chennai 602105, Tamil Nadu, India

来源：

CARDIOMETRY | 2022年 / 25期

关键词：

Machine Learning; Forest Algorithm; Prediction of Heart Disease; Supervised Classification; Novel Principal Component Analysis; Decision Tree;

D O I：

10.18137/cardiometry.2022.25.15201525

中图分类号：

R446 [实验室诊断]; R-33 [实验医学、医学实验];

学科分类号：

1001 ;

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.

引用

页码：1520 / 1525

页数：6

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