An ensemble model for predicting dyslipidemia using 3-years continuous physical examination data.

ABSTRACT:

Background

Dyslipidemia has emerged as a significant clinical risk, with its associated complications, including atherosclerosis and ischemic cerebrovascular disease, presenting a grave threat to human well-being. Hence, it holds paramount importance to precisely predict the onset of dyslipidemia. This study aims to use ensemble technology to establish a machine learning model for the prediction of dyslipidemia.

Methods

This study included three consecutive years of physical examination data of 2,479 participants, and used the physical examination data of the first two years to predict whether the participants would develop dyslipidemia in the third year. Feature selection was conducted through statistical methods and the analysis of mutual information between features. Five

SUBMITTER: Zhang N 

PROVIDER: S-EPMC11540663 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature