An interpretable rule-based diagnostic classification of diabetic nephropathy among type 2 diabetes patients
Open accessAbstract
BACKGROUND: The prevalence of type 2 diabetes is increasing at an alarming rate. Various complications are associated with type 2 diabetes, with diabetic nephropathy being the leading cause of renal failure among diabetics. Often, when patients are diagnosed with diabetic nephropathy, their renal functions have already been significantly damaged. Therefore, a risk prediction tool may be beneficial for the implementation of early treatment and prevention. RESULTS: In the present study, we developed a decision tree-based model integrating genetic and clinical features in a gender-specific classification for the identification of diabetic nephropathy among type 2 diabetic patients. Clinical and genotyping data were obtained from a previous genetic association study involving 345 type 2 diabetic patients (185 with diabetic nephropathy and 160 without diabetic nephropathy). Using a five-fold cross-validation approach, the performance of using clinical or genetic features alone in various classifiers (decision tree, random forest, Naïve Bayes, and support vector machine) was compared with that of utilizing a combination of attributes. The inclusion of genetic features and the implementation of an additional gender-based rule yielded better classification results. CONCLUSIONS: The current model supports the notion that genes and gender are contributing factors of diabetic nephropathy. Further refinement of the proposed approach has the potential to facilitate the early identification of diabetic nephropathy and the development of more efficient treatment in a clinical setting.
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Huang et al. reported that DT, RF, SVM, and BN using clinical and genotyping data for diabetic nephropathy screening yielded accuracy, specificity, and sensitivity of 85.27%, 83.32%, and 85.24% on internal testing, and 78.50%, 80.64%, and 81.40% on a separate testing dataset.
“the proposed method yielded accuracy, specificity, and sensitivity of 85.27%, 83.32, and 85.24%, respectively (internal testing dataset); on a separate testing dataset, the classification accuracy, specificity, and sensitivity were 78.50%, 80.64, and 81.40%, respectively”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as Huang et al.112
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“109 For CKD 39,110,111 and diabetic nephropathy 112,113 screening, studies utilized clinical data, metabolic biomarkers, genotyping data, and/or retinal images to achieve AUCs generally over 0.8.”
MethodInfluentialTarumi, S., Takeuchi, W., Chalkidis, G., Rodriguez-Loya, S., Kuwata, J., Flynn, M., Turner, K. M., Sakaguchi, F. H., Weir, C., Kramer, H., et al. (2021) · Methods of Information in Medicine · doi:10.1055/s-0041-1728757 · Cited by 84 · see citing works
- Authors
- Huang, G.-M., Huang, K.-Y., Lee, T.-Y., & Weng, J. T.-Y.
- Affiliations
- Yuan Ze University (TW)
- Corresponding author
- Weng, Julia Tzu-Ya
- Venue
- BMC Bioinformatics
- Published
- 2015-01-21
- Volume
- 16
- Issue
- S1
- Pages
- S5-S5
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC4331704
- OpenAlex
- W1585367646
- Cited by
- 101
- References
- 1
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 14 · 2023: 13 · 2022: 6 · 2021: 13 · 2020: 14 · 2019: 13 · 2018: 12 · 2017: 5 · 2016: 5 · 2015: 1
- FWCI
- 14.78
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Science Council (NSC 101-2628-E-155-002-MY2)
- Ministry of Science and Technology, Taiwan (MOST103-2221-E155-038)
- 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, type 2 diabetes, risk prediction, decision tree, genetic association, interpretable classification, early diagnosis, renal failure
- MeSH terms
- Bayes Theorem, Decision Trees, Diabetes Mellitus, Type 2, Diabetic Nephropathies, Disease Susceptibility, Female, Genotype, Humans, Male, Sex Factors, Models, Statistical, Risk Assessment, Computational Biology, Support Vector Machine
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