ArticleBMC public health2025
Leveraging XGBoost and explainable AI for accurate prediction of type 2 diabetes.
Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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.
- Explainable machine learning revealing the impact of mental and physical health on arthritis.BMJ health & care informatics · 2026Article
- Development of a prediction model for infectious mononucleosis using machine learning algorithms based on blood cell analysis parameters.BMC infectious diseases · 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
- Machine learning in bleeding risk assessment for low-molecular-weight heparin or fondaparinux: a predictive model study.Scientific reports · 2026Observational
- Dual-Tracer Imaging and Deep Learning for Real-Time Prediction of Lymph Node Metastasis in cN0 Papillary Thyroid Carcinoma.Cancers · 2026Article
- Improved prediction of childhood anemia using hybrid ensemble learning and dual-level explainability.Journal of public health research · 2026Article
- Machine learning prediction model for surgical site infections after major abdominal surgery.Patient safety in surgery · 2026Article
- Enhancing lung cancer classification through a double attention hybrid CNN-HiFuse approach.Scientific reports · 2026Article
- Multivariable AI-based analysis of immune-lifestyle patterns associated with recurrent pregnancy loss: an exploratory retrospective study.Scientific reports · 2026Article
- Different BI-RADS breast cancer diagnosis using MobileNetV1 and vision transformer based on explainable artificial intelligence (XAI).Scientific reports · 2026Article
- Development of a Type 2 Diabetes Prediction Model Using Specific Health Checkup Data and Extraction of Predictive Factors.Bioengineering (Basel, Switzerland) · 2026Article
- Identification of FDA-Approved Drugs as Potential Inhibitors of WEE2: Structure-Based Virtual Screening and Molecular Dynamics with Perspectives for Machine Learning-Assisted Prioritization.Life (Basel, Switzerland) · 2026Article
- Accuracy of dentalmonitoring's artificial intelligence in detecting aligner tracking issues: a retrospective multi-centric study.BMC oral health · 2026Article
- Integrating lipid-related composite indices and explainable machine learning for coronary heart disease risk assessment.Frontiers in public health · 2026Article
- Early Type 2 diabetes risk prediction using explainable machine learning in a two-stage approach.Frontiers in digital health · 2026Article
- Machine learning for predicting emergency department visits in patients with type 2 diabetes: A real-world, multi-institutional study.PloS one · 2026Article
- Multimodal machine learning predicts type 2 respiratory failure in COPD exacerbations: a multicenter XGBoost model with clinical nomogram.Frontiers in medicine · 2026Article
- Age Estimation of the Cervical Vertebrae Region Using Deep Learning.Bioengineering (Basel, Switzerland) · 2025Article
- Machine learning in early screening for high-grade cervical intraepithelial neoplasia using blood testing.BMC medical informatics and decision making · 2025Article
- MMDD: A Multimodal Multitask Dynamic Disentanglement Framework for Robust Major Depressive Disorder Diagnosis Across Neuroimaging Sites.Diagnostics (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionType 2 diabetes mellitus (T2DM) poses a major public health challenge, particularly in regions with limited representation in predictive modeling studies. This research aimed to develop and interpret robust machine learning (ML) models for early T2DM risk prediction using data from the Dena Cohort in Iran.
methodsData from 3,203 adults aged 35–70 years were preprocessed through outlier removal, median/mode imputation, feature selection via LightGBM, and class balancing with the Synthetic Minority Over‑sampling Technique (SMOTE). Two gradient‑boosting algorithms, XGBoost and CatBoost, underwent hyperparameter tuning and 10‑fold cross‑validation. Model performance was assessed using accuracy, F1‑score, and the area under the receiver operating characteristic curve (AUC). Shapley Additive Explanations (SHAP) provided global and case‑specific interpretability of predictive features.
resultsXGBoost achieved the highest performance (accuracy = 96.07%, AUC = 99.29%), outperforming CatBoost and demonstrating substantial improvement with SMOTE balancing. Key predictors included fasting blood sugar, fatty liver, urolithiasis, red blood cell indices, and lifestyle factors such as energy drink consumption and prolonged television viewing. SHAP visualizations enhanced model transparency and facilitated individualized risk interpretation.
conclusionThis study demonstrates that combining advanced gradient‑boosting models with SHAP explainability yields highly accurate, interpretable T2DM risk prediction in an underrepresented population. These findings support integrating interpretable ML into clinical workflows for personalized prevention and early intervention strategies.
Indexed as
Identifiers
What Socratic 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 Socratic graph.