Comparison of Accuracy Rate in Prediction of Cardiovascular Disease using Random Forest with Logistic Regression

被引：0
|
作者：
Vishnuvardhan, Talluri [1 ]
Rama, A. [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：
Prediction of cardiovascular disease; Novel Random forest; Novel Logistic Regression; Smoking; Endocytosis; Hyperglycemia;
D O I：
10.18137/cardiometry.2022.25.15261531
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: Comparison of accuracy rate in prediction of cardiovascular disease using Novel Random Forest with Logistic Regression. Materials and Methods: The Novel Random forest (N=20) and Novel Logistic Regression Algorithm (N=20) these two algorithms are calculated by using 2 Groups and taken 20 samples for both algorithm and accuracy in this work.The sample size is determined using the G power Calculator and it's found to be 10. Results: The Random Forest exhibited 89.06% accuracy whilst a Logistic Regression has shown 92.18%. accuracy. Statistical significance difference between Random forest algorithm and Novel Logistic Regression Algorithm was found to be p=0.001 (2 tailed) (p<0.5). Conclusion: Prediction of cardiovascular disease using Logistic Regression is significantly better than the Random Forest.
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收藏
页码：1526 / 1531
页数：6
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