Comparison of LASSO and random forest models for predicting the risk of premature coronary artery disease.

ABSTRACT:

Purpose

With the change of lifestyle, the occurrence of coronary artery disease presents a younger trend, increasing the medical and economic burden on the family and society. To reduce the burden caused by this disease, this study applied LASSO Logistic Regression and Random Forest to establish a risk prediction model for premature coronary artery disease(PCAD) separately and compared the predictive performance of the two models.

Methods

The data are obtained from 1004 patients with coronary artery disease admitted to a third-class hospital in Liaoning Province from September 2019 to December 2021. The data from 797 patients were ultimately evaluated. The dataset of 797 patients was randomly divided into the training set (569 persons) and the validation set (228 persons) sc

SUBMITTER: Wang J 

PROVIDER: S-EPMC10734117 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature