ReviewDiabetology & metabolic syndrome2021
Machine learning and deep learning predictive models for type 2 diabetes: a systematic review.
Review in Diabetology & metabolic syndrome, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 2 of them syntheses that pooled it.
What it found
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Who cites it
50 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Predictive value of machine learning for the progression of gestational diabetes mellitus to type 2 diabetes: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- The Reporting Quality of Machine Learning Studies on Pediatric Diabetes Mellitus: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Identification of Patient Clusters with Distinct Disease Progression Patterns Utilizing a Nationwide Finnish Population with Type 2 Diabetes.Diabetes therapy : research, treatment and education of diabetes and related disorders · 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
- The adaptive large language models for vaccine prediction: A novel approach to vaccine demand prediction with engineered deviation prompts.PLOS digital health · 2026Article
- Machine-learning-based prediction model of type 2 diabetes using liver enzymes: a cross-sectional study.Frontiers in endocrinology · 2026Article
- Physician versus patient use of AI for diabetes prevention: public perceptions and comfort levels.Frontiers in digital health · 2026Article
- Multidimensional feature fusion in longitudinal physical examination data: a machine learning framework for classification of type 2 diabetes.Frontiers in public health · 2026Article
- Article
- A machine learning tool for predicting newly diagnosed osteoporosis in primary healthcare in the Stockholm Region.Scientific reports · 2025Article
- Toward a Clinically Actionable, Electronic Health Record-Based Machine Learning Model to Forecast 90-Day Change in Hemoglobin AJMIR diabetes · 2025Article
- A machine learning approach for type 2 diabetes diagnosis and prognosis using tailored heterogeneous feature subsets.Medical & biological engineering & computing · 2025Article
- Characterizing the role of early life factors in machine learning-based multimorbidity risk prediction.PLOS digital health · 2025Article
- Advancement of artificial intelligence based treatment strategy in type 2 diabetes: A critical update.Journal of pharmaceutical analysis · 2025Review
- Empowering Pharmacists in Type 2 Diabetes Care: Opportunities for Prevention, Counseling, and Therapeutic Optimization.Journal of clinical medicine · 2025Article
- Applications of AI in Predicting Drug Responses for Type 2 Diabetes.JMIR diabetes · 2025Article
- Machine Learning-Driven D-Glucose Prediction Using a Novel Biosensor for Non-Invasive Diabetes Management.Biosensors · 2025Article
- Exploring Predictors of Type 2 Diabetes Within Animal-Sourced and Plant-Based Dietary Patterns with the XGBoost Machine Learning Classifier: NHANES 2013-2016.Journal of clinical medicine · 2025Article
- Improving T2D machine learning-based prediction accuracy with SNPs and younger age.Computational and structural biotechnology journal · 2025Article
- DiabetesXpertNet: An innovative attention-based CNN for accurate type 2 diabetes prediction.PloS one · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetes Mellitus is a severe, chronic disease that occurs when blood glucose levels rise above certain limits. Over the last years, machine and deep learning techniques have been used to predict diabetes and its complications. However, researchers and developers still face two main challenges when building type 2 diabetes predictive models. First, there is considerable heterogeneity in previous studies regarding techniques used, making it challenging to identify the optimal one. Second, there is a lack of transparency about the features used in the models, which reduces their interpretability. This systematic review aimed at providing answers to the above challenges. The review followed the PRISMA methodology primarily, enriched with the one proposed by Keele and Durham Universities. Ninety studies were included, and the type of model, complementary techniques, dataset, and performance parameters reported were extracted. Eighteen different types of models were compared, with tree-based algorithms showing top performances. Deep Neural Networks proved suboptimal, despite their ability to deal with big and dirty data. Balancing data and feature selection techniques proved helpful to increase the model's efficiency. Models trained on tidy datasets achieved almost perfect models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.