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Advanced Data Analytics in eHealth (391) Clinical Information and Decision Making (3987) Decision Support for Health Professionals (2413) Machine Learning (3404) Hypertension Prevention and Treatment (366) Artificial Intelligence (5152) Artificial Intelligence in Diabetes Care and Prevention (119) Nephrology/Renal Disorders (155)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/64979, first published .
Stethoscope, glucose meter, and lancets on a wooden table for diabetes monitoring.

Interpretable Machine Learning Model for Predicting and Assessing the Risk of Diabetic Nephropathy: Prediction Model Study

Interpretable Machine Learning Model for Predicting and Assessing the Risk of Diabetic Nephropathy: Prediction Model Study

Authors of this article:

Yili Wen1 Author Orcid Image ;   Zhiqiang Wan2 Author Orcid Image ;   Huiling Ren1 Author Orcid Image ;   Xu Wang1 Author Orcid Image ;   Weijie Wang1 Author Orcid Image
Article Authors Cited by (12) Tweetations Metrics

Journals

  1. Li Q, Cheng M, Zhang X. Review on the Gut‐Kidney Axis Mechanism and Therapeutic Strategies in Diabetic Nephropathy. Diabetes/Metabolism Research and Reviews 2026;42(1) View
  2. Ajith T. Emerging Biomarkers in Diabetic Kidney Disease: The Necessity for Future Validation Studies, Mechanistic Investigation, and Integration into Risk Models. Journal of Advanced Health Research & Clinical Medicine 2025;2(2):41 View
  3. Li D, Liu X, Zhang L, Gong Q, Huang W, Xu Y. From the energy factory to the intercellular communication medium: the emerging role of intercellular mitochondrial transfer and mitochondrial transplantation therapy in diabetes and diabetic complications. Journal of Translational Medicine 2026;24(1) View
  4. Zhang M, Zhu S, Hu D, Fu W, Hu H, Xu Y, Zhang K, Tang H, Du X. Developing and validating a clinlabomics-based machine-learning model for early detection of occult diabetic kidney disease: implications for primary care screening. Frontiers in Endocrinology 2026;17 View
  5. Kang B, Oh R, Kim T, Yoo J, Kang D, Kim G, Jin S, Chung M, Kim J. Association of Variability of Monthly Continuous Glucose Monitoring‐Derived Metrics With Diabetic Kidney Disease in Type 1 Diabetes. Diabetes, Obesity and Metabolism 2026;28(10):9044 View
  6. Zhou C, Wu X, Tian Z, Mo L, Liu F, Li J. Machine learning prediction models for diabetic kidney disease: A systematic review and meta-analysis. Experimental Gerontology 2026;224:113304 View
  7. Khan T, Saeed M, Hussain M, Khan L, Khan M, nadeem A, Alghamdi T. A comparative and interpretable machine learning framework for reliable diabetes risk prediction. Scientific Reports 2026;16(1) View
  8. Lukhele N, Mostafa F. Development of an explainable machine learning framework for early diabetes detection, multiclass classification, and risk stratification across age groups. Frontiers in Digital Health 2026;8 View
  9. Arceo Díaz S, Bricio Barrios E, Trujillo-Trujillo X, Sánchez-García S, Bricio Barrios J, Ríos Silva M, Huerta Viera M. Predictive models of anthropometric parameters for primary screening of sarcopenia based on Machine Learning. Revista Mexicana de Ingeniería Biomédica 2026;47(2):e1565 View
  10. Erdem M, Aydemir O. Explainable machine learning for predicting kidney disease progression defined by changes in estimated glomerular filtration rate in type 2 diabetes: a single-centre retrospective study. Frontiers in Endocrinology 2026;17 View
  11. Song J, Li J, Zhang H, Ding J, Wei K, Ma X, He B, Zhou R, Zhou H, Zhou Q, Cao T, Dong P, Wu B, Ke W, Yang D, Zhou J, Deng Q, Sun Y, Yao Y, Zi H, Zheng L, Zhang J, Chen Q, Chen X, Lin Y, Chen S. Blood pressure phenotypes and kidney impairment in adults with diabetes: a machine learning study. Frontiers in Aging 2026;7 View

Conference Proceedings

  1. Abid M, Morshed A, Kabir M, Polak M, Myisha N, Ghosh S. 2026 Second International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI). Cost-Aware Early Treatment Response Stratification in Non-Small Cell Lung Cancer Using Interpretable Machine Learning View