ArticleJournal of the American Medical Informatics Association : JAMIA2020
A combined strategy of feature selection and machine learning to identify predictors of prediabetes.
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction models for progression from prediabetes to diabetes: a systematic review and meta-analysis.Frontiers in endocrinology · 2026Pooled it
- Screening Tools for Early Identification of Adults at High Risk of Type 2 Diabetes: A Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- Mapping neighbourhood-level drivers of type 2 diabetes for precision public health using predictive and causal machine learning.Scientific reports · 2026Article
- Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review.Annals of translational medicine · 2025Review
- Optimizing Feature Selection and Machine Learning Algorithms for Early Detection of Prediabetes Risk: Comparative Study.JMIR bioinformatics and biotechnology · 2025Article
- Applications of Artificial Intelligence and Machine Learning in Prediabetes: A Scoping Review.Journal of diabetes science and technology · 2025Review
- 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
- Development, validation and recalibration of a prediction model for prediabetes: an EHR and NHANES-based study.BMC medical informatics and decision making · 2024Article
- Characteristics and risk factors associated with developing prediabetes in Saudi Arabia.Annals of medicine · 2024Article
- Uric acid is associated with type 2 diabetes: data mining approaches.Diabetology international · 2024Article
- Machine Learning-Based Prediction of Binge Drinking among Adults in the United State: Analysis of the 2022 Health Information National Trends Survey.Proceedings of the 2024 9th International Conference on Mathematics and Artificial Intelligence · 2024Article
- Identifying diagnostic indicators for type 2 diabetes mellitus from physical examination using interpretable machine learning approach.Frontiers in endocrinology · 2024Article
- Prediction of Prednisolone Dose Correction Using Machine Learning.Journal of healthcare informatics research · 2023Article
- Environmental exposures in machine learning and data mining approaches to diabetes etiology: A scoping review.Artificial intelligence in medicine · 2023Article
- Machine Learning in Nutrition Research.Advances in nutrition (Bethesda, Md.) · 2022Article
- Hyperglycemia screening based on survey data: an international instrument based on WHO STEPs dataset.BMC endocrine disorders · 2022Article
- Identification of Prediabetes Discussions in Unstructured Clinical Documentation: Validation of a Natural Language Processing Algorithm.JMIR medical informatics · 2022Article
- Identifying Glucose Metabolism Status in Nondiabetic Japanese Adults Using Machine Learning Model with Simple Questionnaire.Computational and mathematical methods in medicine · 2022Article
- Machine learning for diabetes clinical decision support: a review.Advances in computational intelligence · 2022Review
- A Cardiovascular Disease Prediction Model Based on Routine Physical Examination Indicators Using Machine Learning Methods: A Cohort Study.Frontiers in cardiovascular medicine · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo identify predictors of prediabetes using feature selection and machine learning on a nationally representative sample of the US population. MATERIALS AND
methodsWe analyzed n = 6346 men and women enrolled in the National Health and Nutrition Examination Survey 2013-2014. Prediabetes was defined using American Diabetes Association guidelines. The sample was randomly partitioned to training (n = 3174) and internal validation (n = 3172) sets. Feature selection algorithms were run on training data containing 156 preselected exposure variables. Four machine learning algorithms were applied on 46 exposure variables in original and resampled training datasets built using 4 resampling methods. Predictive models were tested on internal validation data (n = 3172) and external validation data (n = 3000) prepared from National Health and Nutrition Examination Survey 2011-2012. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC). Predictors were assessed by odds ratios in logistic models and variable importance in others. The Centers for Disease Control (CDC) prediabetes screening tool was the benchmark to compare model performance.
resultsPrediabetes prevalence was 23.43%. The CDC prediabetes screening tool produced 64.40% AUROC. Seven optimal (≥ 70% AUROC) models identified 25 predictors including 4 potentially novel associations; 20 by both logistic and other nonlinear/ensemble models and 5 solely by the latter. All optimal models outperformed the CDC prediabetes screening tool (P < 0.05). DISCUSSION: Combined use of feature selection and machine learning increased predictive performance outperforming the recommended screening tool. A range of predictors of prediabetes was identified.
conclusionThis work demonstrated the value of combining feature selection with machine learning to identify a wide range of predictors that could enhance prediabetes prediction and clinical decision-making.
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