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<p>Cognitive impairment (CI) is an emerging public health challenge in rural aging populations, where access to formal cognitive testing is limited. Using data from 4781 adults in the Rural Chinese Cohort Study (RCCS) with 10-year follow-up and the Shenzhen Aging-related Disorder Cohort (SADC) with baseline cognitive assessment, this study developed an interpretable machine learning model for long-term CI risk prediction and evaluated its cross-population transportability using eight routinely collected indicators. Among nine algorithms, the Random Forest model showed the most balanced performance, with AUCs of 0.721 (95% CI: 0.596, 0.846) in the RCCS internal validation set for 10-year incident CI prediction and 0.690 (95% CI: 0.664, 0.717) in the SADC baseline-CI evaluation, acceptable calibration, and positive net benefit across threshold probabilities of ~10–40%. SHAP identified age, education, and systolic blood pressure as the main predictive contributors. Subgroup analyses showed heterogeneous discrimination, while sensitivity analyses supported broadly consistent performance under alternative data partitioning and MICE imputation. Mediation analysis suggested a primarily direct association between baseline age and later CI. These findings indicate that a simple, interpretable model based on widely available clinical indicators may support preliminary risk stratification and targeted cognitive screening in resource-constrained rural settings.</p>

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Explainable machine learning for 10-year prediction of cognitive impairment in a rural Chinese population

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月15日

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  • Botang Guo,
  • Shiqi Li,
  • Minyao Li,
  • Yuanshuo Ma,
  • Ying Fu,
  • Yihao Shu,
  • Yaqin Su,
  • Kexin Li,
  • Jinliang Liang,
  • Taifeng Chen,
  • Li Yang,
  • Zibo Ni,
  • Yang Zhao,
  • Xueru Fu,
  • Yuying Wu,
  • Liuding Wen,
  • Weifeng Huo,
  • Haocheng Yang,
  • Fulan Hu,
  • Ming Zhang,
  • Yu Liu,
  • Ping Tang,
  • Lei Yin,
  • Dongsheng Hu

摘要

Cognitive impairment (CI) is an emerging public health challenge in rural aging populations, where access to formal cognitive testing is limited. Using data from 4781 adults in the Rural Chinese Cohort Study (RCCS) with 10-year follow-up and the Shenzhen Aging-related Disorder Cohort (SADC) with baseline cognitive assessment, this study developed an interpretable machine learning model for long-term CI risk prediction and evaluated its cross-population transportability using eight routinely collected indicators. Among nine algorithms, the Random Forest model showed the most balanced performance, with AUCs of 0.721 (95% CI: 0.596, 0.846) in the RCCS internal validation set for 10-year incident CI prediction and 0.690 (95% CI: 0.664, 0.717) in the SADC baseline-CI evaluation, acceptable calibration, and positive net benefit across threshold probabilities of ~10–40%. SHAP identified age, education, and systolic blood pressure as the main predictive contributors. Subgroup analyses showed heterogeneous discrimination, while sensitivity analyses supported broadly consistent performance under alternative data partitioning and MICE imputation. Mediation analysis suggested a primarily direct association between baseline age and later CI. These findings indicate that a simple, interpretable model based on widely available clinical indicators may support preliminary risk stratification and targeted cognitive screening in resource-constrained rural settings.