ArticlePreventing chronic disease2019
Building Risk Prediction Models for Type 2 Diabetes Using Machine Learning Techniques.
Article in Preventing chronic disease, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Mapping neighbourhood-level drivers of type 2 diabetes for precision public health using predictive and causal machine learning.Scientific reports · 2026Article
- Machine-learning-based prediction model of type 2 diabetes using liver enzymes: a cross-sectional study.Frontiers in endocrinology · 2026Article
- Machine learning models for predicting new-onset diabetes following acute pancreatitis using real-world data.BMJ public health · 2026Article
- Statistical and machine-learning assessment of attitudinal, knowledge, and perceptual factors on diabetes awareness in Kuwait.BMC medical informatics and decision making · 2025Article
- Sex-Specific Dietary Predictors of Blood Glucose Identified Through Decision Tree Modeling in Adults.Nutrients · 2025Article
- Explainable machine learning model incorporating social determinants of health to predict chronic kidney disease in type 2 diabetes patients.Journal of diabetes and metabolic disorders · 2025Article
- Survey-Based Machine Learning Models for Early Detection of Diabetes.The 7th International Conference on Wireless, Intelligent and Distributed Environment for Communication : WIDECOM 2024. International Conference on Wireless, Intelligent and Distributed Environment for Communication (7th : 2024 : Keene,... · 2025Article
- AI-driven analysis of diabetes risk determinants in U.S. adults: Exploring disease prevalence and health factors.PloS one · 2025Article
- Article
- Bias in machine learning applications to address non-communicable diseases at a population-level: a scoping review.BMC public health · 2024Article
- Stacking model framework reveals clinical biochemical data and dietary behavior features associated with type 2 diabetes: A retrospective cohort study.APL bioengineering · 2024Article
- Machine Learning Meets Meta-Heuristics: Bald Eagle Search Optimization and Red Deer Optimization for Feature Selection in Type II Diabetes Diagnosis.Bioengineering (Basel, Switzerland) · 2024Article
- A feature optimization study based on a diabetes risk questionnaire.Frontiers in public health · 2024Article
- Correlation analysis of diabetes based on Copula.Frontiers in endocrinology · 2024Article
- A Practical Approach to Disease Risk Prediction: Focus on High-Risk Patients via Highest-Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2023Article
- Enhancement of Classifier Performance Using Swarm Intelligence in Detection of Diabetes from Pancreatic Microarray Gene Data.Biomimetics (Basel, Switzerland) · 2023Article
- Diabetes risk prediction model based on community follow-up data using machine learning.Preventive medicine reports · 2023Article
- Prediction Performance of Feature Selectors and Classifiers on Highly Dimensional Transcriptomic Data for Prediction of Weight Loss in Filipino Americans at Risk for Type 2 Diabetes.Biological research for nursing · 2023Review
- Prediction of Weight Loss to Decrease the Risk for Type 2 Diabetes Using Multidimensional Data in Filipino Americans: Secondary Analysis.JMIR diabetes · 2023Article
- Identification of the risk factors of type 2 diabetes and its prediction using machine learning techniques.Health systems (Basingstoke, England) · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
introductionAs one of the most prevalent chronic diseases in the United States, diabetes, especially type 2 diabetes, affects the health of millions of people and puts an enormous financial burden on the US economy. We aimed to develop predictive models to identify risk factors for type 2 diabetes, which could help facilitate early diagnosis and intervention and also reduce medical costs.
methodsWe analyzed cross-sectional data on 138,146 participants, including 20,467 with type 2 diabetes, from the 2014 Behavioral Risk Factor Surveillance System. We built several machine learning models for predicting type 2 diabetes, including support vector machine, decision tree, logistic regression, random forest, neural network, and Gaussian Naive Bayes classifiers. We used univariable and multivariable weighted logistic regression models to investigate the associations of potential risk factors with type 2 diabetes.
resultsAll predictive models for type 2 diabetes achieved a high area under the curve (AUC), ranging from 0.7182 to 0.7949. Although the neural network model had the highest accuracy (82.4%), specificity (90.2%), and AUC (0.7949), the decision tree model had the highest sensitivity (51.6%) for type 2 diabetes. We found that people who slept 9 or more hours per day (adjusted odds ratio [aOR] = 1.13, 95% confidence interval [CI], 1.03-1.25) or had checkup frequency of less than 1 year (aOR = 2.31, 95% CI, 1.86-2.85) had higher risk for type 2 diabetes.
conclusionOf the 8 predictive models, the neural network model gave the best model performance with the highest AUC value; however, the decision tree model is preferred for initial screening for type 2 diabetes because it had the highest sensitivity and, therefore, detection rate. We confirmed previously reported risk factors and also identified sleeping time and frequency of checkup as 2 new potential risk factors related to type 2 diabetes.
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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.