ArticleHealthcare (Basel, Switzerland)2021
A Machine Learning Approach to Predicting Diabetes Complications.
Article in Healthcare (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 3 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine learning in the prediction of diabetic peripheral neuropathy: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature.Journal of nursing management · 2022Pooled it
- The application of machine learning for infant cries classification and pathological cries detection: A systematic review.Science progressPooled it
- Advances in the application of artificial intelligence-driven multi-modal imaging technologies in the comprehensive diagnosis and treatment of diabetic foot ulcers.Reviews in endocrine & metabolic disorders · 2026Review
- A Stacked Ensemble Multi-Label Model for Predicting Co-Occurring Microvascular and Macrovascular Complications in Type 2 Diabetes.Chronic diseases and translational medicine · 2026Article
- Complication Risk Classification in Children and Adolescents With Type 1 Diabetes: Interpretable Machine Learning Study Based on Saudi Clinical Guidelines.JMIR formative research · 2026Article
- An Effective Model-Based Voting Classifier for Diabetes Mellitus Classification.Bioengineering (Basel, Switzerland) · 2026Article
- Association of Disease Knowledge, Comorbidity, and Biochemical Parameters in Adults with Type 2 Diabetes.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Article
- An ethnic-sensitive hybrid framework for T2D prediction with explainable AI and weighted ensembles.Scientific reports · 2025Article
- Evolution of diabetes prediction using the fusion of ANOVA, ADASYN technique and XGBoost based on body composition data.Journal of diabetes and metabolic disorders · 2025Article
- Continuous Glucose Monitoring Data Analysis 2.0: Functional Data Pattern Recognition and Artificial Intelligence Applications.Journal of diabetes science and technology · 2025Article
- Evaluation of Machine Learning Model Performance in Diabetic Foot Ulcer: Retrospective Cohort Study.JMIR medical informatics · 2025Article
- Machine learning with the body roundness index and associated indicators: a new approach to predicting metabolic syndrome.BMC public health · 2025Article
- Engineering novel features for diabetes complication prediction using synthetic electronic health records.Frontiers in genetics · 2025Article
- Optimized prediction of diabetes complications using ensemble learning with Bayesian optimization: a cost-efficient laboratory-based approach.Frontiers in endocrinology · 2025Article
- Interpretable multimodal machine learning (IMML) framework reveals pathological signatures of distal sensorimotor polyneuropathy.Communications medicine · 2024Article
- Article
- Machine learning techniques to predict the risk of developing diabetic nephropathy: a literature review.Journal of diabetes and metabolic disorders · 2024Article
- The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century.Bioengineering (Basel, Switzerland) · 2024Review
- Predictive model and risk analysis for peripheral vascular disease in type 2 diabetes mellitus patients using machine learning and shapley additive explanation.Frontiers in endocrinology · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
Diabetes mellitus (DM) is a chronic disease that is considered to be life-threatening. It can affect any part of the body over time, resulting in serious complications such as nephropathy, neuropathy, and retinopathy. In this work, several supervised classification algorithms were applied for building different models to predict and classify eight diabetes complications. The complications include metabolic syndrome, dyslipidemia, neuropathy, nephropathy, diabetic foot, hypertension, obesity, and retinopathy. For this study, a dataset collected by the Rashid Center for Diabetes and Research (RCDR) located in Ajman, UAE, was utilized. The dataset consists of 884 records with 79 features. Some essential preprocessing steps were applied to handle the missing values and unbalanced data problems. Furthermore, feature selection was performed to select the top five and ten features for each complication. The final number of records used to train and build the binary classifiers for each complication was as follows: 428-metabolic syndrome, 836-dyslipidemia, 223-neuropathy, 233-nephropathy, 240-diabetic foot, 586-hypertension, 498-obesity, 228-retinopathy. Repeated stratified k-fold cross-validation (with k = 10 and a total of 10 repetitions) was employed for a better estimation of the performance. Accuracy and F1-score were used to evaluate the models' performance reaching a maximum of 97.8% and 97.7% for accuracy and F1-scores, respectively. Moreover, by comparing the performance achieved using different attributes' sets, it was found that by using a selected number of features, we can still build adequate classifiers.
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