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Cited by 8Artificial Intelligence in HealthcareChronic Kidney Disease and DiabetesData Mining and Machine Learning Applications

Development of an intelligent clinical decision support system for the early prediction of diabetic nephropathy

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Afrash, Mohammad Reza, Rahimi, Fatemeh, Kazemi-Arpanahi, Hadi, Shanbezadeh, Mostafa, Amraei, Morteza, Asadi, Farkhondeh·2022·Informatics in Medicine Unlocked
DOI 10.1016/j.imu.2022.101135
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Abstract

Diabetic nephropathy (DN) is the most common microvascular complication of diabetes mellitus (DM) and is identified as a leading cause of the end-stage renal disease (ESRD). Considering the importance of early prediction of individuals at risk of this complication, the use of intelligent models through machine learning (ML) algorithms can be helpful. Therefore, this study aimed to identify the influential variables in predicting DN and fed them as inputs to develop an ML-based decision support system (DSS) for DN diagnosis. The data of 327 patients with diabetes (types 1 and 2) were retrospectively analyzed. After data preparation, the genetic algorithm (GA) feature selection method was used to identify the predictor variables affecting DN. Then, several ML algorithms, including the support vector machine (SVM), decision tree (DT), K-nearest neighbors (KNN), and artificial neural networks (ANN) were used to train predictive models based on the selected features. Afterward, the performance of the developed models was evaluated using sensitivity, specificity, and accuracy criteria in 10 independent runs. Finally, the DSS was developed based on the best fit model in the C# programming language. Our findings illustrated that age, hemoglobin A1c (HbA1c) test, diastolic arterial pressure (DAP), systolic arterial pressure (SAP), fasting glycemia rate (FGR), and DM involvement time were the most important factors in predicting DN. Moreover, to predict the DN, GA combined with the DT algorithm obtained the highest performance in terms of accuracy, sensitivity, specificity, and area under the curve (AUC), equal to 98.9, 98.6, 99.2, and 98.9%, respectively. The results revealed that GA combined with the DT classifier predicted DN with significant accuracy. Thus, the DSS developed based on DT can be considered a reliable tool to help physicians make decisions. Future studies are warranted to further validate the applicability of our model in clinical settings.

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  • Afrash et al. demonstrated that a DT-based clinical feature model achieved superior discriminatory performance in predicting DN progression risk.

    “Afrash et al. [71] demonstrated that a DT-based clinical feature model achieved superior discriminatory performance in predicting DN progression risk”

    From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [71]

    The Results (section 3.3) report that the DT model outperformed five other models on sensitivity and accuracy, with an ROC AUC of 98.9%. The model predicts whether patients with diabetes will develop DN; the cited passages do not establish that it predicts progression of existing DN. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • A retrospective study employing DTs and GAs highlighted the predictive value of age, HbA1c, SBP and DBP for DN.

    “Similarly, a retrospective study employing decision trees (DTs) and genetic algorithms (GAs) highlighted the predictive value of age, HbA1c, systolic and diastolic blood pressure (SBP and DBP) [71].”

    From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [71]

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Authors
Afrash, M. R., Rahimi, F., Kazemi-Arpanahi, H., Shanbezadeh, M., Amraei, M., & Asadi, F.
Affiliations
  • Shahid Beheshti University of Medical Sciences (IR)
  • Abadan University of Medical Sciences (IR)
  • Lorestan University of Medical Sciences (IR)
Corresponding author
  • Rahimi, Fatemeh
  • Asadi, Farkhondeh
ORCID
Venue
Informatics in Medicine Unlocked
Publisher
Elsevier BV
Published
2022
Volume
35
Pages
101135
ISSN
2352-9148 (ISSN-L)
DOI
10.1016/j.imu.2022.101135
OpenAlex
W4309875130
Cited by
8
References
52
Citations by year
2026: 4 · 2025: 2 · 2024: 1 · 2023: 1
FWCI
0.75
Type
journal article
Language
en
Open access
Licence
Locations
  • Informatics in Medicine Unlocked (publishedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Research Repository Portal (Ilam University of Medical Sciences) (submittedVersion)
Topics
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Nephrology › Chronic Kidney Disease and Diabetes
  • Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
Keywords
diabetic nephropathy, early prediction, clinical decision support systems, diabetes mellitus, genetic algorithm feature selection, decision tree classification, support vector machine, artificial neural networks, k-nearest neighbors, HbA1c, blood pressure, fasting blood glucose, disease duration, microvascular complications

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