ArticleInternational journal of environmental research and public health2022
Machine Learning Models for Data-Driven Prediction of Diabetes by Lifestyle Type.
Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 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
- An Optimized and Explainable Machine Learning Framework for Diabetes Prediction Using Marine Predators Algorithm and SHAP.Journal of personalized medicine · 2026Article
- An Interpretable Fuzzy Distance-Based Ensemble Framework with SHAP Analysis for Clinically Transparent Prediction of Diabetes.Diagnostics (Basel, Switzerland) · 2026Article
- A new extended belief rule base method based on neighborhood covering reduction for diabetes diagnosis.PloS one · 2026Article
- Machine learning models for predicting new-onset diabetes following acute pancreatitis using real-world data.BMJ public health · 2026Article
- Machine learning models for chronic disease risks using complex survey data: the impact of sample weights.Frontiers in digital health · 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
- Machine learning and Mendelian randomization identify key lifestyle factors in coronary heart disease: A NHANES-Based study.International journal of cardiology. Cardiovascular risk and prevention · 2025Article
- Context matters in machine learning based disease prediction with insights from diverse clinical and symptom data.Scientific reports · 2025Article
- Statistical and machine-learning assessment of attitudinal, knowledge, and perceptual factors on diabetes awareness in Kuwait.BMC medical informatics and decision making · 2025Article
- Machine learning-based risk factor analysis and prediction model construction for mortality in chronic heart failure.Journal of global health · 2025Article
- Machine Learning-driven Identification of the Honeymoon Phase in Pediatric Type 1 Diabetes and Optimizing Insulin ManagementJournal of clinical research in pediatric endocrinology · 2025Article
- Evaluating AUC estimators across complex sampling designs: insights from COVID-19 patient data.BMC medical research methodology · 2025Article
- Article
- An interpretable machine learning model with demographic variables and dietary patterns for ASCVD identification: from U.S. NHANES 1999-2018.BMC medical informatics and decision making · 2025Article
- Predicting diabetes self-management education engagement: machine learning algorithms and models.BMJ open diabetes research & care · 2025Article
- Exploring Predictors of Type 2 Diabetes Within Animal-Sourced and Plant-Based Dietary Patterns with the XGBoost Machine Learning Classifier: NHANES 2013-2016.Journal of clinical medicine · 2025Article
- Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults.Frontiers in nutrition · 2025Article
- Prediction of subjective well-being level in residents of Dali City: Where modern tourism meets traditional ethnic culture.PloS one · 2025Article
- Article
Corrections and comments
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Authors and funding
10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The prevalence of diabetes has been increasing in recent years, and previous research has found that machine-learning models are good diabetes prediction tools. The purpose of this study was to compare the efficacy of five different machine-learning models for diabetes prediction using lifestyle data from the National Health and Nutrition Examination Survey (NHANES) database. The 1999-2020 NHANES database yielded data on 17,833 individuals data based on demographic characteristics and lifestyle-related variables. To screen training data for machine models, the Akaike Information Criterion (AIC) forward propagation algorithm was utilized. For predicting diabetes, five machine-learning models (CATBoost, XGBoost, Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM)) were developed. Model performance was evaluated using accuracy, sensitivity, specificity, precision, F1 score, and receiver operating characteristic (ROC) curve. Among the five machine-learning models, the dietary intake levels of energy, carbohydrate, and fat, contributed the most to the prediction of diabetes patients. In terms of model performance, CATBoost ranks higher than RF, LG, XGBoost, and SVM. The best-performing machine-learning model among the five is CATBoost, which achieves an accuracy of 82.1% and an AUC of 0.83. Machine-learning models based on NHANES data can assist medical institutions in identifying diabetes patients.
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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.