Research graph
References from Using machine learning and explainable AI to explore dietary factors associated with type 2 diabetes: a cross-sectional study from the UK NDNS. Local targets link to admitted publications; unresolved targets remain external evidence.
Global, regional, and national burden of type 2 diabetes mellitus caused by high BMI from 1990 to 2021, and forecasts to 2045: analysis from the global burden of disease study 2021
10.3389/fpubh.2025.1515797 · 2025 · External reference
Independent and Joint Associations of BMI and Waist Circumference With the Onset of Type 2 Diabetes Mellitus in Chinese Adults: Prospective Data Linkage Study
2023 · External reference
The Role of Nutrition in the Prevention and Intervention of Type 2 Diabetes
10.3389/fbioe.2020.575442 · 2020 · External reference
Role of diet in type 2 diabetes incidence: umbrella review of meta-analyses of prospective observational studies
10.1136/bmj.l2368 · 2019 · External reference
Etiologic effects and optimal intakes of foods and nutrients for risk of cardiovascular diseases and diabetes: Systematic reviews and meta-analyses from the Nutrition and Chronic Diseases Expert Group (NutriCoDE)
10.1371/journal.pone.0175149 · 2017 · External reference
Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives
10.1186/s40001-025-03196-w · 2025 · External reference
Enhancing cancer zone diagnosis in MRI images: A novel SOM neural network approach with block processing in the presence of noise
2025 · External reference
Artificial intelligence in polycystic ovary syndrome: a systematic review of diagnostic and predictive applications
10.1186/s12911-025-03255-6 · 2025 · External reference
Healthcare predictive analytics using machine learning and deep learning techniques: a survey
10.1186/s43067-023-00108-y · 2023 · External reference
Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis
10.1093/ehjdh/ztae080 · 2025 · External reference
Identifying top ten predictors of type 2 diabetes through machine learning analysis of UK Biobank data
10.1038/s41598-024-52023-5 · 2024 · External reference
Identification of the risk factors of type 2 diabetes and its prediction using machine learning techniques
10.1080/20476965.2022.2141141 · 2023 · External reference
Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches
10.3390/ijerph18147346 · 2021 · External reference
Leveraging XGBoost and explainable AI for accurate prediction of type 2 diabetes
10.1186/s12889-025-24953-w · 2025 · External reference
Explainable extratreeclassifier model for early detection of type 2 diabetes: evidence from the PERSIAN Dena Cohort
10.1186/s12911-025-03333-9 · 2025 · External reference
Machine Learning Models Integrating Dietary Indicators Improve the Prediction of Progression from Prediabetes to Type 2 Diabetes Mellitus
10.3390/nu17060947 · 2025 · External reference
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Explainable artificial intelligence to assess hospital financial performance during the COVID-19 pandemic through operating margin metric
10.1108/jhom-04-2025-0207 · 2026 · External reference
10.1023/a:1010933404324
10.1023/a:1010933404324 · External reference
10.1613/jair.953
10.1613/jair.953 · External reference
10.1201/9788743801306-3
10.1201/9788743801306-3 · External reference
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10.2337/dc24-sint
10.2337/dc24-sint · External reference
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Dietary Determinants of Diabetes Prevalence: A Cross-Sectional Study in the Hungarian Population
10.3390/nu18050731 · 2026 · External reference
10.1007/s11222-021-10057-z
10.1007/s11222-021-10057-z · External reference
Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development
10.1111/cts.70056 · 2024 · External reference
Statistical stability indices for LIME: Obtaining reliable explanations for machine learning models
10.1080/01605682.2020.1865846 · 2022 · External reference
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