Physical frailty identification using machine learning to explore the 5-item FRAIL scale, Cardiovascular Health Study index, and Study of Osteoporotic Fractures index.

Journal: Frontiers in public health
PMID: 38784574

Abstract

BACKGROUND: Physical frailty is an important issue in aging societies. Three models of physical frailty assessment, the 5-Item fatigue, resistance, ambulation, illness and loss of weight (FRAIL); Cardiovascular Health Study (CHS); and Study of Osteoporotic Fractures (SOF) indices, have been regularly used in clinical and research studies. However, no previous studies have investigated the predictive ability of machine learning (ML) for physical frailty assessment. The aim was to use two ML algorithms, random forest (RF) and extreme gradient boosting (XGBoost), to predict these three physical frailty assessment models.

Authors

  • Chen-Cheng Yang
    Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung City, Taiwan.
  • Po-Hong Chen
    Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City, Taiwan.
  • Cheng-Hong Yang
    Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan. chyang@cc.kuas.edu.tw.
  • Chia-Yen Dai
    Department of Internal Medicine, Faculty of Medicine, Kaohsiung Medical University, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
  • Kuei-Hau Luo
    Department of Occupational and Environmental Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung City, Taiwan.
  • Tzu-Hua Chen
    Department of Family Medicine, Kaohsiung Municipal Ta-tung Hospital, Kaohsiung Medical University, Kaohsiung City, Taiwan.
  • Hung-Yi Chuang
    Department of Occupational and Environmental Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung City, Taiwan.
  • Chao-Hung Kuo
    Department of Internal Medicine, Kaohsiung Municipal Siaogang Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan; Division of Gastroenterology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan; Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan. Electronic address: kjh88kmu@gmail.com.

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