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Cited by 36Chronic Kidney Disease and DiabetesArtificial Intelligence in HealthcareAcute Kidney Injury Research

Explainable Machine Learning‐Based Prediction Model for Diabetic Nephropathy

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Yin, Jing-Mei, Li, Yang, Xue, Jun-Tang, Zong, Guo-Wei, Fang, Zhong-Ze, Zou, Lang·2024·Journal of Diabetes Research
DOI 10.1155/2024/8857453PMC PMC10821806
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Abstract

The aim of this study is to analyze the effect of serum metabolites on diabetic nephropathy (DN) and predict the prevalence of DN through a machine learning approach. The dataset consists of 548 patients from April 2018 to April 2019 in the Second Affiliated Hospital of Dalian Medical University (SAHDMU). We select the optimal 38 features through a least absolute shrinkage and selection operator (LASSO) regression model and a 10‐fold cross‐validation. We compare four machine learning algorithms, including extreme gradient boosting (XGB), random forest, decision tree, and logistic regression, by AUC‐ROC curves, decision curves, and calibration curves. We quantify feature importance and interaction effects in the optimal predictive model by Shapley additive explanation (SHAP) method. The XGB model has the best performance to screen for DN with the highest AUC value of 0.966. The XGB model also gains more clinical net benefits than others, and the fitting degree is better. In addition, there are significant interactions between serum metabolites and duration of diabetes. We develop a predictive model by XGB algorithm to screen for DN. C2, C5DC, Tyr, Ser, Met, C24, C4DC, and Cys have great contribution in the model and can possibly be biomarkers for DN.

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  • Yin et al. developed an XGBoost-based DN prediction model with serum metabolomics data, achieving an AUC of 0.966 and identifying eight potential metabolic biomarkers including acetylcarnitine, glutarylcarnitine, tyrosine, serine, and methionine.

    “Yin et al. [79] developed an XGBoost-based DN prediction model with serum metabolomics data, achieving an AUC of 0.966 and identifying eight potential metabolic biomarkers including acetylcarnitine, glutarylcarnitine, tyrosine, serine, and methionine.”

    From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [79]

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Cited by

Sorted by citations

Authors
Yin, J.-M., Li, Y., Xue, J.-T., Zong, G.-W., Fang, Z.-Z., & Zou, L.
Affiliations
  • Xiangtan University (CN)
  • Tianjin Medical University (CN)
Corresponding author
  • Fang, Zhong-Ze
  • Zou, Lang
ORCID
Venue
Journal of Diabetes Research
Publisher
Wiley
Published
2024-01
Volume
2024
Issue
1
Pages
8857453-8857453
ISSN
  • 2314-6745 (ISSN-L)
  • 2314-6753
DOI
10.1155/2024/8857453
PMC
PMC10821806
OpenAlex
W4391047834
Cited by
36
References
2
Citations by year
2026: 17 · 2025: 13 · 2024: 5 · 2023: 1
FWCI
9.98
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Diabetes Research (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
Funders
  • Education Department of Hunan Province (23B0178)
  • National Natural Science Foundation of China (82273676)
  • National Key Research and Development Program of China (2021YFA1301202)
  • Xiangtan University
Topics
  • Health Sciences › Medicine › Nephrology › Chronic Kidney Disease and Diabetes
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Nephrology › Acute Kidney Injury Research
Keywords
diabetic nephropathy, serum metabolites, disease prediction, extreme gradient boosting, SHAP, explainable machine learning, biomarkers, diabetes duration, feature interaction, LASSO feature selection, random forest, decision tree, logistic regression
MeSH terms
Machine Learning, Algorithms, Calibration, Diabetes Mellitus, Diabetic Nephropathies, Hospitals, University, Humans

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