Scholarlink

<p>Early identification of diabetes in older adults is essential for preventing complications, yet many high‑risk individuals remain undetected in community settings. Using recent cycles of the nationally representative Korea National Health and Nutrition Examination Survey (KNHANES 2020–2023), we developed and temporally validated an Extreme Gradient Boosting (XGBoost) model to rule-in diabetes among Korean adults aged ≥ 50&#xa0;years. Candidate predictors included sociodemographic factors, health behaviors, anthropometric indices, blood pressure, medical history, and simple laboratory markers. Data from 2020 to 2022 were used for model development, with the 2023 cycle reserved as a temporal external validation cohort. We prespecified a high‑specificity rule‑in threshold based on the development cohort and evaluated discrimination (area under the receiver operating characteristic curve (AUROC) and average precision), calibration, Brier score, classification metrics, decision‑curve net benefit, and Shapley additive explanation (SHAP) values. In temporal external validation, the XGBoost model demonstrated robust performance (AUROC 0.868; average precision 0.646; Brier score 0.101) and achieved high rule-in accuracy (0.866), specificity (97.3%), positive predictive value (76.7%), and F1-score (0.521) at the prespecified threshold. Compared with logistic regression and random forest, the model showed superior rule-in performance and performed comparably to Light Gradient Boosting Machine (LightGBM), a gradient boosting framework based on decision tree ensembles, in terms of specificity and positive predictive value, while intentionally accepting reduced sensitivity consistent with a high-specificity design. SHAP analyses identified urine creatinine, urine specific gravity, urine albumin, total cholesterol and other lipids, body mass index, waist circumference, and a history of hypertension and dyslipidemia as major contributors to model predictions. These findings indicate that an XGBoost-based rule-in model using routinely collected survey variables can efficiently identify older Korean adults with a high probability of diabetes and may serve as a practical decision-support tool for prioritizing confirmatory testing and targeted screening in community settings with limited resources.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Development and temporal external validation of a high-specificity XGBoost rule-in model for diabetes in middle-aged and older Korean adults

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月1日

查看全文
  • Soo Myeong Kim,
  • Jung Min Cho

摘要

Early identification of diabetes in older adults is essential for preventing complications, yet many high‑risk individuals remain undetected in community settings. Using recent cycles of the nationally representative Korea National Health and Nutrition Examination Survey (KNHANES 2020–2023), we developed and temporally validated an Extreme Gradient Boosting (XGBoost) model to rule-in diabetes among Korean adults aged ≥ 50 years. Candidate predictors included sociodemographic factors, health behaviors, anthropometric indices, blood pressure, medical history, and simple laboratory markers. Data from 2020 to 2022 were used for model development, with the 2023 cycle reserved as a temporal external validation cohort. We prespecified a high‑specificity rule‑in threshold based on the development cohort and evaluated discrimination (area under the receiver operating characteristic curve (AUROC) and average precision), calibration, Brier score, classification metrics, decision‑curve net benefit, and Shapley additive explanation (SHAP) values. In temporal external validation, the XGBoost model demonstrated robust performance (AUROC 0.868; average precision 0.646; Brier score 0.101) and achieved high rule-in accuracy (0.866), specificity (97.3%), positive predictive value (76.7%), and F1-score (0.521) at the prespecified threshold. Compared with logistic regression and random forest, the model showed superior rule-in performance and performed comparably to Light Gradient Boosting Machine (LightGBM), a gradient boosting framework based on decision tree ensembles, in terms of specificity and positive predictive value, while intentionally accepting reduced sensitivity consistent with a high-specificity design. SHAP analyses identified urine creatinine, urine specific gravity, urine albumin, total cholesterol and other lipids, body mass index, waist circumference, and a history of hypertension and dyslipidemia as major contributors to model predictions. These findings indicate that an XGBoost-based rule-in model using routinely collected survey variables can efficiently identify older Korean adults with a high probability of diabetes and may serve as a practical decision-support tool for prioritizing confirmatory testing and targeted screening in community settings with limited resources.