Jing Wang

Predicting metabolic syndrome in adolescents from device-measured 24-h movement behaviors: a machine learning approach

To develop and internally evaluate a non-invasive machine-learning model for identifying metabolic syndrome (MS) in adolescents using 24-h movement behaviors and non-invasively measured physical characteristics. This cross-sectional study included 822 adolescents aged 12–15 years recruited in Tianjin, China,...