ArticleScientific reports2024
Identifying top ten predictors of type 2 diabetes through machine learning analysis of UK Biobank data.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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33 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.
- Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis.Frontiers in digital health · 2025Pooled it
- Evaluation and Comparison of Machine Learning Methods for Type 2 Diabetes Classification and Associated Factors.Healthcare informatics research · 2026Article
- Identification of Patient Clusters with Distinct Disease Progression Patterns Utilizing a Nationwide Finnish Population with Type 2 Diabetes.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2026Article
- Machine Learning Integration of Clinical and Molecular Biomarkers to Predict Vascular Complications in Type 2 Diabetes.Diagnostics (Basel, Switzerland) · 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
- An Effective Model-Based Voting Classifier for Diabetes Mellitus Classification.Bioengineering (Basel, Switzerland) · 2026Article
- InterFeat: a pipeline for finding interesting scientific features.Scientific reports · 2026Article
- Access to care affects electronic health record reliability and AI-driven disease prediction.Nature health · 2026Article
- Central adiposity outperforms disordered eating behavior in predicting type 2 diabetes risk in young adults: an explainable machine learning approach.Frontiers in public health · 2026Article
- A machine learning framework for predictive interpretation of variants of uncertain significance in hereditary cancer.Frontiers in systems biology · 2026Article
- Plasminogen activator inhibitor-1, vaspin, and dietary inflammatory index in relation to cardiometabolic health in diabetic women.Frontiers in nutrition · 2026Article
- Propolis supplementation improves cardiometabolic health in patients with type 2 diabetes mellitus: findings from a GRADE-assessed systematic review and meta-analysis of RCTs.Journal of diabetes and metabolic disorders · 2025Review
- Opportunistic AI-derived adiposity measures from coronary artery calcium scans predict new-onset type 2 diabetes in adults without obesity or hyperglycemia: insights from the AI-CVD study within MESA.Diabetology & metabolic syndrome · 2025Article
- Predictive Models for Type 2 Diabetes Mellitus in Han Chinese with Insights into Cross-Population Applicability and Demographic Specific Risk Factors.Diabetes & metabolism journal · 2025Article
- Risk for Diabetes From Long Working Hours and Night Work in the United States: Prospective Associations and Machine Learning Techniques.Safety and health at work · 2025Article
- Sex- and age-specific determinants of diabetes: Insights from BKMR and cox modelling of metabolic and lifestyle risk factors in a Korean cohort.Diabetes, obesity & metabolism · 2025Article
- Development and Validation of DIANA (Diabetes Novel Subgroup Assessment tool): A web-based precision medicine tool to determine type 2 diabetes endotype membership and predict individuals at risk of microvascular disease.PLOS digital health · 2025Article
- Characterizing the role of early life factors in machine learning-based multimorbidity risk prediction.PLOS digital health · 2025Article
- Data-driven diabetes mellitus prediction and management: a comparative evaluation of decision tree classifier and artificial neural network models along with statistical analysis.Scientific reports · 2025Article
- A cost-effectiveness analysis alongside trial of a digital health-supported and community pharmacy-based prediabetes management programme (PRIME Programme) in Malaysia.Diabetes, obesity & metabolism · 2025Article
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4 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
The study aimed to identify the most predictive factors for the development of type 2 diabetes. Using an XGboost classification model, we projected type 2 diabetes incidence over a 10-year horizon. We deliberately minimized the selection of baseline factors to fully exploit the rich dataset from the UK Biobank. The predictive value of features was assessed using shap values, with model performance evaluated via Receiver Operating Characteristic Area Under the Curve, sensitivity, and specificity. Data from the UK Biobank, encompassing a vast population with comprehensive demographic and health data, was employed. The study enrolled 450,000 participants aged 40-69, excluding those with pre-existing diabetes. Among 448,277 participants, 12,148 developed type 2 diabetes within a decade. HbA1c emerged as the foremost predictor, followed by BMI, waist circumference, blood glucose, family history of diabetes, gamma-glutamyl transferase, waist-hip ratio, HDL cholesterol, age, and urate. Our XGboost model achieved a Receiver Operating Characteristic Area Under the Curve of 0.9 for 10-year type 2 diabetes prediction, with a reduced 10-feature model achieving 0.88. Easily measurable biological factors surpassed traditional risk factors like diet, physical activity, and socioeconomic status in predicting type 2 diabetes. Furthermore, high prediction accuracy could be maintained using just the top 10 biological factors, with additional ones offering marginal improvements. These findings underscore the significance of biological markers in type 2 diabetes prediction.
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