ArticleInternational journal of environmental research and public health2021
Prediction of Type 2 Diabetes Based on Machine Learning Algorithm.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 1 of them a synthesis that pooled it.
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Who cites it
79 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis.Frontiers in digital health · 2025Pooled it
- Data-driven cluster analysis of adults with prediabetes: Findings from the Japan diabetes prevention study.Journal of diabetes investigation · 2026Trial
- A retrospective cohort study identifies differential diabetes progression among Han, Dai, and Yi groups in Yunnan.iScience · 2026Article
- Identifying Alzheimer's Disease Progression Subphenotypes Via a Graph-based Framework Using Electronic Health Records.Journal of healthcare informatics research · 2026Article
- Comparison of the predictive power of novel insulin resistance, lipid metabolism and obesity indices for the risk of developing dyslipidaemia in adults.BMC endocrine disorders · 2026Article
- Article
- Prediction of Recurrence After Endometrial Polypectomy in Women of Childbearing Age Based on Machine Learning Algorithm.International journal of women's health · 2026Article
- Artificial Intelligence Tools in Myocardial Infarction Prognosis: Evaluating the Performance of Machine Learning and Deep Learning Models.Current cardiology reviews · 2026Review
- Salivary Metal Ions as Potential Biomarkers for Diabetes: An Observational Study.International journal of dentistry · 2026Article
- Machine learning predicts diabetes risk in high-risk populations: analysis of National Health and Nutrition Examination Survey data.Archives of medical science : AMS · 2026Article
- Multidimensional feature fusion in longitudinal physical examination data: a machine learning framework for classification of type 2 diabetes.Frontiers in public health · 2026Article
- An XGBoost-based model for detecting undiagnosed type 2 diabetes using routine physical and lifestyle data from a multi-center Chinese population.Frontiers in medicine · 2026Article
- Machine learning for predicting emergency department visits in patients with type 2 diabetes: A real-world, multi-institutional study.PloS one · 2026Article
- Machine-learning-based prediction model of type 2 diabetes using liver enzymes: a cross-sectional study.Frontiers in endocrinology · 2026Article
- A modular deep learning architecture for interpretable disease prediction across tabular clinical and biometric datasets.PloS one · 2026Article
- Non-Glycemic Clinical Data for Type 2 Diabetes Detection in Mexican Adults: A Comparative Analysis of Atherogenic Indices, Statistical Transformations, and Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2025Article
- Characterize Disease Progression Subphenotypes in Real World Populations with Overweight and Obesity using a Graph-based Neural Network Framework.medRxiv : the preprint server for health sciences · 2025Article
- Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review.Annals of translational medicine · 2025Review
- Type 2 Diabetes Prediction Model in China: A Five-Year Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Applications of Artificial Intelligence and Machine Learning in Prediabetes: A Scoping Review.Journal of diabetes science and technology · 2025Review
19 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction of type 2 diabetes (T2D) occurrence allows a person at risk to take actions that can prevent onset or delay the progression of the disease. In this study, we developed a machine learning (ML) model to predict T2D occurrence in the following year (Y + 1) using variables in the current year (Y). The dataset for this study was collected at a private medical institute as electronic health records from 2013 to 2018. To construct the prediction model, key features were first selected using ANOVA tests, chi-squared tests, and recursive feature elimination methods. The resultant features were fasting plasma glucose (FPG), HbA1c, triglycerides, BMI, gamma-GTP, age, uric acid, sex, smoking, drinking, physical activity, and family history. We then employed logistic regression, random forest, support vector machine, XGBoost, and ensemble machine learning algorithms based on these variables to predict the outcome as normal (non-diabetic), prediabetes, or diabetes. Based on the experimental results, the performance of the prediction model proved to be reasonably good at forecasting the occurrence of T2D in the Korean population. The model can provide clinicians and patients with valuable predictive information on the likelihood of developing T2D. The cross-validation (CV) results showed that the ensemble models had a superior performance to that of the single models. The CV performance of the prediction models was improved by incorporating more medical history from the dataset.
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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.