Cardiometry | |
An innovative penalty based heart disease prediction system using novel random forest over logistic regression classifier algorithm | |
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: Novel Random Forest; Logistic Regression; Data Mining; Blood pressure; Pulse rate; Heart Disease; Classification; | |
DOI : 10.18137/cardiometry.2022.25.14771482 | |
学科分类:环境科学(综合) | |
来源: Russian New University | |
【 摘 要 】
Aim:The main goal of the research is see how accurately predicting heart disease by Logistic Regression (LR) and Novel Random Forest(RF) Classifications. Materials and Methods: Novel Random forest appealed on a heart dataset which consists of 200 recordsA framework for predicting heart disease in the medical field has been proposed and developed to compare the RF with a LR classifier. The sample size was calculated to be 55 for each group with 80% G performance. The sample size was calculated using a Clincalc analysis with Alpha and Beta values of 0.05 and 0.5, pretest performance of 80%, and enrollment rate of 1. The Accuracy of the classifier was Evaluated and Recorded. Results: The LR produces 89.0% in predicting the heart disease on the data set used whereas the Novel Random forest classifier predicts the same at the rate of 95.46% of the time with a statistically significant difference between the two groups (P=0.03; P<0.05) with confidence interval 95%. Conclusion: RF is better compared with LR in terms of both precision and accuracy.
【 授权许可】
CC BY
【 预 览 】
Files | Size | Format | View |
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RO202307120003618ZK.pdf | 249KB | download |