ArticleComputational and mathematical methods in medicine2014
Screening for prediabetes using machine learning models.
Article in Computational and mathematical methods in medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.
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Who cites it
43 citing papers in PubMed, 1 synthesis or guideline pooled it, 108 citations in OpenAlex.
- A dual domain systematic review and meta-analysis of risk tool accuracy to predict cardiovascular morbidity in prehypertension and diabetic morbidity in prediabetes.Frontiers in endocrinology · 2025Pooled it
- Screening Tools for Early Identification of Adults at High Risk of Type 2 Diabetes: A Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- Early viability assessment of a Business-to-Consumer (B2C) model for digital diabetes screening in Switzerland.BMC health services research · 2026Article
- Mapping neighbourhood-level drivers of type 2 diabetes for precision public health using predictive and causal machine learning.Scientific reports · 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-based prediction of fever among under-five children in Ethiopia: A national-level study.PloS one · 2026Article
- A predictive study of glycaemic reversal in Chinese individuals with prediabetes based on machine learning: a 5-year cohort study.Frontiers in endocrinology · 2026Article
- Artificial intelligence model as a tool to predict prediabetes.Scientific reports · 2025Article
- Applications of Artificial Intelligence and Machine Learning in Prediabetes: A Scoping Review.Journal of diabetes science and technology · 2025Review
- Identifying determinants of malnutrition in under-five children in Bangladesh: insights from the BDHS-2022 cross-sectional study.Scientific reports · 2025Article
- Interpretable machine learning method to predict the risk of pre-diabetes using a national-wide cross-sectional data: evidence from CHNS.BMC public health · 2025Article
- Improving T2D machine learning-based prediction accuracy with SNPs and younger age.Computational and structural biotechnology journal · 2025Article
- Group-informed attentive framework for enhanced diabetes mellitus progression prediction.Frontiers in endocrinology · 2024Article
- Risk Prediction of Diabetes Progression Using Big Data Mining with Multifarious Physical Examination Indicators.Diabetes, metabolic syndrome and obesity : targets and therapy · 2024Article
- Machine learning for predicting diabetes risk in western China adults.Diabetology & metabolic syndrome · 2023Article
- Article
- Value of machine learning algorithms for predicting diabetes risk: A subset analysis from a real-world retrospective cohort study.Journal of diabetes investigation · 2023Article
- Environmental exposures in machine learning and data mining approaches to diabetes etiology: A scoping review.Artificial intelligence in medicine · 2023Article
- Hyperglycemia screening based on survey data: an international instrument based on WHO STEPs dataset.BMC endocrine disorders · 2022Article
- Tracking Health, Performance and Recovery in Athletes Using Machine Learning.Sports (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The global prevalence of diabetes is rapidly increasing. Studies support the necessity of screening and interventions for prediabetes, which could result in serious complications and diabetes. This study aimed at developing an intelligence-based screening model for prediabetes. Data from the Korean National Health and Nutrition Examination Survey (KNHANES) were used, excluding subjects with diabetes. The KNHANES 2010 data (n = 4685) were used for training and internal validation, while data from KNHANES 2011 (n = 4566) were used for external validation. We developed two models to screen for prediabetes using an artificial neural network (ANN) and support vector machine (SVM) and performed a systematic evaluation of the models using internal and external validation. We compared the performance of our models with that of a screening score model based on logistic regression analysis for prediabetes that had been developed previously. The SVM model showed the areas under the curve of 0.731 in the external datasets, which is higher than those of the ANN model (0.729) and the screening score model (0.712), respectively. The prescreening methods developed in this study performed better than the screening score model that had been developed previously and may be more effective method for prediabetes screening.
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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 Socratic graph.