Predicting risk of obesity in overweight adults using interpretable machine learning algorithms.

ABSTRACT:

Objective

To screen for predictive obesity factors in overweight populations using an optimal and interpretable machine learning algorithm.

Methods

This cross-sectional study was conducted between June 2011 and January 2012. The participants were randomly selected using a simple random sampling technique. Seven commonly used machine learning methods were employed to construct obesity risk prediction models. A total of 5,236 Chinese participants from Ningde City, Fujian Province, Southeast China, participated in this study. The best model was selected through appropriate verification and validation and suitably explained. Subsequently, a minimal set of significant predictors was identified. The Shapley additive explanation force plot was used to illustrate the model at the in

SUBMITTER: Lin W 

PROVIDER: S-EPMC10693451 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature