ArticleScientific reports2023
Comparisons of the prediction models for undiagnosed diabetes between machine learning versus traditional statistical methods.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of machine learning approaches for pediatric asthma exacerbation management: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Modeling the Determinants of Subjective Well-Being in Schizophrenia.Schizophrenia bulletin · 2025Trial
- Predicting 90-day mortality in patients with HBV-ACLF: A comparison of machine learning models and logistic regression.Medicine · 2026Article
- Quantifying the Predictive Power of Social Determinants of Health in Cardiovascular Disease and Type 2 Diabetes Progression Using XGBoost: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- Early prediction of sepsis in the ICU: a comparative analysis of multiple machine-learning algorithms using the MIMIC-III database.BMC medical informatics and decision making · 2026Article
- Development and temporal external validation of a high-specificity XGBoost rule-in model for diabetes in middle-aged and older Korean adults.Diabetology & metabolic syndrome · 2026Article
- Mapping neighbourhood-level drivers of type 2 diabetes for precision public health using predictive and causal machine learning.Scientific reports · 2026Article
- Development and internal validation of a post-retrieval machine learning models for OHSS risk stratification in assisted reproductive technology: an exploratory study.Frontiers in endocrinology · 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
- Article
- Use of Automated Machine Learning to Detect Undiagnosed Diabetes in US Adults: Development and Validation Study.JMIR AI · 2025Article
- Artificial Intelligence Enabled Lifestyle Medicine in Diabetes Care: A Narrative Review.American journal of lifestyle medicine · 2025Review
- Incidence and predictors of type 2 diabetes mellitus in a population-based cohort study in Abu Dhabi.Scientific reports · 2025Article
- Prediction Models for Postoperative Delirium of Cardiovascular Surgery (PODOCVS): Protocol for a Systematic Review.JMIR research protocols · 2025Article
- Machine Learning for Predicting the Transition From Gestational Diabetes to Type 2 Diabetes: A Systematic Review.Cureus · 2025Review
- Article
- Research on Prediction model of Carotid-Femoral Pulse Wave Velocity: Based on Machine Learning Algorithm.Journal of clinical hypertension (Greenwich, Conn.) · 2025Article
- Improving T2D machine learning-based prediction accuracy with SNPs and younger age.Computational and structural biotechnology journal · 2025Article
- Federated multimodal AI for precision-equitable diabetes care.Frontiers in digital health · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We compared the prediction performance of machine learning-based undiagnosed diabetes prediction models with that of traditional statistics-based prediction models. We used the 2014-2020 Korean National Health and Nutrition Examination Survey (KNHANES) (N = 32,827). The KNHANES 2014-2018 data were used as training and internal validation sets and the 2019-2020 data as external validation sets. The receiver operating characteristic curve area under the curve (AUC) was used to compare the prediction performance of the machine learning-based and the traditional statistics-based prediction models. Using sex, age, resting heart rate, and waist circumference as features, the machine learning-based model showed a higher AUC (0.788 vs. 0.740) than that of the traditional statistical-based prediction model. Using sex, age, waist circumference, family history of diabetes, hypertension, alcohol consumption, and smoking status as features, the machine learning-based prediction model showed a higher AUC (0.802 vs. 0.759) than the traditional statistical-based prediction model. The machine learning-based prediction model using features for maximum prediction performance showed a higher AUC (0.819 vs. 0.765) than the traditional statistical-based prediction model. Machine learning-based prediction models using anthropometric and lifestyle measurements may outperform the traditional statistics-based prediction models in predicting undiagnosed diabetes.
Indexed as
Identifiers
What OpenQuestion holds
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.