Predictive modeling the probability of suffering from metabolic syndrome using machine learning: A population-based study.

ABSTRACT:

Background

There is an increasing trend of Metabolic syndrome (MetS) prevalence, which has been considered as an important contributor for cardiovascular disease (CVD), cancers and diabetes. However, there is often a long asymptomatic phase of MetS, resulting in not diagnosed and intervened so timely as needed. It would be very helpful to explore tools to predict the probability of suffering from MetS in daily life or routinely clinical practice.

Objective

To develop models that predict individuals' probability of suffering from MetS timely with high efficacy in general population.

Methods

The present study enrolled 8964 individuals aged 40-75 years without severe diseases, which was a part of the REACTION study from October 2011 to February 2012. We developed three p

SUBMITTER: Hu X 

PROVIDER: S-EPMC9834713 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature