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

Journal: Endocrine
PMID: 37776483

Abstract

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.

Authors

  • Wei Lin
    Department of Geriatric Rehabilitation, Jiangbin Hospital, Nanning, China.
  • Songchang Shi
    Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Huiyu Lan
    Department of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, FuZhou, 350001, PR China.
  • Nengying Wang
    Department of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, FuZhou, 350001, PR China.
  • Huibin Huang
    Department of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, FuZhou, 350001, PR China.
  • Junping Wen
    Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China.
  • Gang Chen
    Department of Orthopedics, West China Hospital, Sichuan University, Chengdu, Sichuan, China.

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