Machine learning-based predictive Modeling of diabetic nephropathy in type 2 diabetes using integrated biomarkers: A single-Center retrospective study
Open accessAbstract
Purpose: Diabetic nephropathy (DN), a major complication of diabetes mellitus, significantly impacts global health. Identifying individuals at risk of developing DN is crucial for early intervention and improving patient outcomes. This study aims to develop and validate a machine learning-based predictive model using integrated biomarkers. Methods: A cross-sectional analysis was conducted on a baseline dataset involving 2184 participants without DN, categorized based on their development of DN over a follow-up period of 36 months: DN (n=1270) and Non-DN (n=914). Various demographic and clinical parameters were analyzed. The findings were validated using an independent dataset comprising 468 participants, with 273 developing DN and 195 remaining as Non-DN over the follow-up period. Machine learning algorithms, alongside traditional descriptive statistics and logistic regression were used for statistical analyses. Results: Elevated levels of serum creatinine, urea, and reduced eGFR, alongside an increased prevalence of retinopathy and peripheral neuropathy, were prominently observed in those who developed DN. Validation on the independent dataset further confirmed the model's robustness and consistency. The SVM model demonstrated superior performance in the training set (AUC=0.79, F1-score=0.74) and testing set (AUC=0.83, F1-score=0.82), outperforming other models. Significant predictors of DN included serum creatinine, eGFR, presence of diabetic retinopathy, and peripheral neuropathy. Conclusion: Integrating machine learning algorithms with clinical and biomarker data at baseline offers a promising avenue for identifying individuals at risk of developing diabetic nephropathy in type 2 diabetes patients over a 36-month period.
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Most DN clinical feature-based studies did not specify the prediction horizon, with only a few targeting new-onset DN over 1- or 3-year periods.
“Notably, most studies did not specify the prediction horizon, with only a few targeting new-onset DN over 1- or 3-year periods [65, 70].”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [65]
The Abstract Methods and Methods > Data Collection and Variables describe patients initially free of DN who were followed for 36 months to identify new cases. This supports the paper as an example of a three-year prediction study, but it does not establish that most other studies omitted a prediction horizon or address the one-year studies. (LLM, conf=0.86)
Experimental — accuracy not yet measuredZhu et al. analyzed historical EHR data from 2184 Chinese T2D patients and developed an SVM-based model for DN prediction.
“Zhu et al. [65] analyzed historical EHR data from 2184 Chinese T2D patients and developed an SVM-based model for DN prediction.”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [65]
Zhu et al. developed a SVM-based risk prediction model for DN complications based on real-world EHR data from China.
“Zhu et al. [65] developed a SVM-based risk prediction model for DN complications based on real-world EHR data from China.”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [65]
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- Authors
- Zhu, Y., Zhang, Y., Yang, M., Tang, N., Liu, L., Wu, J., & Yang, Y.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Corresponding author
- Yang, Yan
- Venue
- Diabetes, Metabolic Syndrome and Obesity
- Publisher
- Informa UK Limited
- Published
- 2024-05
- Volume
- Volume 17
- Pages
- 1987-1997
- ISSN
- 1178-7007 (ISSN-L)
- PMC
- PMC11093114
- OpenAlex
- W4396811038
- Cited by
- 14
- References
- 27
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 1
- FWCI
- 3.99
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Diabetes Metabolic Syndrome and Obesity (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Dove Medical Press (Taylor and Francis Group) (submittedVersion, cc-by-nc)
- Topics
- Health Sciences › Medicine › Nephrology › Chronic Kidney Disease and Diabetes
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- diabetic nephropathy, type 2 diabetes, risk prediction, predictive modeling, serum creatinine, estimated glomerular filtration rate, diabetic retinopathy, peripheral neuropathy, support vector machine, early detection
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