Explainable machine learning identifies key quality-of-life-related predictors of arthritis status: evidence from the China health and retirement longitudinal study.

Journal: Health and quality of life outcomes
Published Date: Aug 26, 2025

Abstract

BACKGROUND: Arthritis is a prevalent chronic disease substantially impacting patients' quality of life (QoL). While identifying key determinants associated with arthritis is critical for targeted interventions, traditional statistical methods often struggle with complex interactions, and existing machine learning (ML) approaches frequently lack the interpretability needed to guide clinical decisions. This study integrates a comprehensive, explainable machine learning (XAI) workflow to identify and interpret key QoL-related predictors of arthritis status in a large national cohort.

Authors

  • Kaibin Lin
    School of Computer Science, Hunan First Normal University, Changsha, 410205, China.
  • Tingting Jiang
    Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, 06511, CT, USA.
  • Jiafen Liao
    Department of Rheumatology and Immunology, The Second Xiangya, Hospital of Central South University, Changsha, China.
  • Xianrun Zhou
    School of Computer Science, Hunan First Normal University, Changsha, China.
  • Zheng Wang
    Department of Infectious Diseases, Renmin Hospital of Wuhan University, Wuhan 430060, China.
  • Yiyue Chen
    Clinical Medical Research Center for Systemic Autoimmune Diseases in Hunan Province, Changsha, China.
  • Xi Xu
    School of Medicine, Yangtze University, Jingzhou 434000, China.
  • Bing Zhou
    Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou 450001, China.

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