ArticleBMC bioinformatics2015
An interpretable rule-based diagnostic classification of diabetic nephropathy among type 2 diabetes patients.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of Machine Learning Classification Models for the Prediction of Type 2 Diabetes Mellitus: A Systematic Survey and Meta-Analysis Approach.International journal of environmental research and public health · 2022Pooled it
- An Algorithm for Identifying Microvascular Complications in Type 1 Diabetes in the Clinical Setting: Development and External Validation.Health data science · 2026Article
- Artificial Intelligence for Diabetes Complication Prediction: A Systematic Review of Current Applications and Future Directions.Journal of diabetes science and technology · 2025Review
- Explainable Diagnosis Prediction through Neuro-Symbolic Integration.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Evaluating Feature Selection Methods for Accurate Diagnosis of Diabetic Kidney Disease.Biomedicines · 2024Article
- Machine Learning Meets Meta-Heuristics: Bald Eagle Search Optimization and Red Deer Optimization for Feature Selection in Type II Diabetes Diagnosis.Bioengineering (Basel, Switzerland) · 2024Article
- Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable Cities.Bioengineering (Basel, Switzerland) · 2024Article
- The automatic detection of diabetic kidney disease from retinal vascular parameters combined with clinical variables using artificial intelligence in type-2 diabetes patients.BMC medical informatics and decision making · 2023Article
- Artificial intelligence in diabetes management: Advancements, opportunities, and challenges.Cell reports. Medicine · 2023Review
- Artificial Intelligence and Infectious Disease Imaging.The Journal of infectious diseases · 2023Article
- Machine learning based study for the classification of Type 2 diabetes mellitus subtypes.BioData mining · 2023Article
- Machine Learning Models to Predict the Risk of Rapidly Progressive Kidney Disease and the Need for Nephrology Referral in Adult Patients with Type 2 Diabetes.International journal of environmental research and public health · 2023Article
- A Cyclopentanone Compound Attenuates the Over-Accumulation of Extracellular Matrix and Fibrosis in Diabetic Nephropathy via Downregulating the TGF-β/p38MAPK Axis.Biomedicines · 2022Article
- Objective study of the facial parameters of observations in patients with type 2 diabetes mellitus by machine learning.Annals of translational medicine · 2022Article
- Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus.Methods of information in medicine · 2021Article
- Identification of risk factors for patients with diabetes: diabetic polyneuropathy case study.BMC medical informatics and decision making · 2020Article
- Serum and urine metabolomics reveal potential biomarkers of T2DM patients with nephropathy.Annals of translational medicine · 2020Article
- Artificial Intelligence Applications in Type 2 Diabetes Mellitus Care: Focus on Machine Learning Methods.Healthcare informatics research · 2019Review
- Group 2 Innate Lymphoid Cells Participate in Renal Fibrosis in Diabetic Kidney Disease Partly via TGF-Journal of diabetes research · 2019Article
- The Role of ERRFI1+808T/G Polymorphism in Diabetic Nephropathy.International journal of molecular and cellular medicine · 2019Article
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Authors and funding
4 authors.
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Abstract
backgroundThe 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.
resultsIn 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.
conclusionsThe 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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