Identification of influence factors in overweight population through an interpretable risk model based on machine learning: a large retrospective cohort

• Published in: Endocrine Vol. 83; no. 3; pp. 604 - 614
• Main Authors: Lin, Wei, Shi, Songchang, Lan, Huiyu, Wang, Nengying, Huang, Huibin, Wen, Junping, Chen, Gang
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2024; Springer Nature B.V
• Subjects: Body weight; China - epidemiology; Diabetes; East Asian People; Endocrinology; Humanities and Social Sciences; Humans; Internal Medicine; Learning algorithms; Machine Learning; Medicine; Medicine & Public Health; multidisciplinary; Original Article; Overweight; Overweight - epidemiology; Retrospective Studies; Risk Factors; Science; Visual discrimination learning; China; Risk; Interpretable; Prediction model; Machine learning; Overweight

Background The identification of associated overweight risk factors is crucial to future health risk predictions and behavioral interventions. Several consensus problems remain in machine learning, such as cross-validation, and the resulting model may suffer from overfitting or poor interpretability...