Age-specific risk factors for the prediction of obesity using a machine learning approach.

ABSTRACT: Machine Learning is a powerful tool to discover hidden information and relationships in various data-driven research fields. Obesity is an extremely complex topic, involving biological, physiological, psychological, and environmental factors. One successful approach to the topic is machine learning frameworks, which can reveal complex and essential risk factors of obesity. Over the last two decades, the obese population (BMI of above 23) in Korea has grown. The purpose of this study is to identify risk factors that predict obesity using machine learning classifiers and identify the algorithm with the best accuracy among classifiers used for obesity prediction. This work will allow people to assess obesity risk from blood tests and blood pressure data based on the KNHANES, which used data c

SUBMITTER: Jeon J 

PROVIDER: S-EPMC9887184 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature