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Background and aims <p>Early detection of chronic kidney disease (CKD) in high-altitude regions remains difficult due to physiological adaptations that may affect conventional biomarkers. This study aimed to develop machine learning-based diagnostic models tailored to high-altitude populations.</p> Materials and methods <p>This retrospective cohort study included 19,068 individuals living in high-altitude regions (≈ 2300&#xa0;m) who attended Qinghai Provincial People’s Hospital between 2019 and 2022. Regression models were constructed to estimate glomerular filtration rate (GFR) as a continuous outcome, while classification models were developed to diagnose CKD based on KDIGO criteria. Artificial neural networks (ANN), LASSO regression, ridge regression, and linear regression were implemented for GFR prediction. A subgroup of 289 patients undergoing 99mTc-DTPA renal dynamic imaging served as a physiological reference for external model assessment. Classification algorithms included logistic regression, k-nearest neighbors (KNN), support vector machines (SVM), decision trees, naive Bayes, random forest (RF), and ANN. The dataset was split into training (80%) and testing (20%) cohorts using stratified sampling. Five-fold cross-validation was applied for hyperparameter tuning. Model performance was evaluated using AUC, correlation coefficients, precision, recall, and calibration metrics.</p> Results <p>In regression models predicting GFR, ANN achieved the highest performance (AUC = 0.87), outperforming the CKD-EPI formula. In the 99mTc-DTPA subgroup, ridge regression showed the best discrimination (AUC = 0.88). In classification models, RF demonstrated superior performance (AUC = 0.92). All ML models outperformed traditional GFR equations.</p> Conclusion <p>Machine learning models significantly improve diagnostic accuracy of CKD in high-altitude populations. ANN and RF models demonstrated promising predictive capability, supporting their potential clinical application in high-altitude regions.</p>

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Development of a high-altitude renal disease diagnostic model based on machine learning and multiple biomarker detection: a retrospective study of 19,068 patients

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月26日

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  • Chenhao Liu,
  • Menglin Luo,
  • Sergio Benardini,
  • Xinyao Ji,
  • Yuheng Xiao,
  • Yuli Luo,
  • Wei Yan,
  • Feng Zheng,
  • Zi-an Li,
  • Changchun Niu

摘要

Background and aims

Early detection of chronic kidney disease (CKD) in high-altitude regions remains difficult due to physiological adaptations that may affect conventional biomarkers. This study aimed to develop machine learning-based diagnostic models tailored to high-altitude populations.

Materials and methods

This retrospective cohort study included 19,068 individuals living in high-altitude regions (≈ 2300 m) who attended Qinghai Provincial People’s Hospital between 2019 and 2022. Regression models were constructed to estimate glomerular filtration rate (GFR) as a continuous outcome, while classification models were developed to diagnose CKD based on KDIGO criteria. Artificial neural networks (ANN), LASSO regression, ridge regression, and linear regression were implemented for GFR prediction. A subgroup of 289 patients undergoing 99mTc-DTPA renal dynamic imaging served as a physiological reference for external model assessment. Classification algorithms included logistic regression, k-nearest neighbors (KNN), support vector machines (SVM), decision trees, naive Bayes, random forest (RF), and ANN. The dataset was split into training (80%) and testing (20%) cohorts using stratified sampling. Five-fold cross-validation was applied for hyperparameter tuning. Model performance was evaluated using AUC, correlation coefficients, precision, recall, and calibration metrics.

Results

In regression models predicting GFR, ANN achieved the highest performance (AUC = 0.87), outperforming the CKD-EPI formula. In the 99mTc-DTPA subgroup, ridge regression showed the best discrimination (AUC = 0.88). In classification models, RF demonstrated superior performance (AUC = 0.92). All ML models outperformed traditional GFR equations.

Conclusion

Machine learning models significantly improve diagnostic accuracy of CKD in high-altitude populations. ANN and RF models demonstrated promising predictive capability, supporting their potential clinical application in high-altitude regions.