Improving T2D machine learning-based prediction accuracy with SNPs and younger age.

Background: This study aimed to evaluate whether integrating clinical and genomic data improves the performance of machine learning (ML) models for predicting Type 2 Diabetes (T2D) risk. Methods: Six models-Random Forest, Support Vector Machine, Linear Discriminant Analysis, Logistic Regression, Gradient Boosting Machine, and Decision Tree-were trained and tested on a discovery dataset (N=3,546) and validated in the UK Biobank (N=31,620). Model performance was assessed using clinical data alone, combined clinical and genomic data, and in age-specific groups (>55 and ≤55 years). Results: The inclusion of genomic data modestly improved model performance across all algorithms in the discovery dataset. Clinical features such as family history of T2D and hypertension consistently ranked as top features. When SNPs were added, T2D-associated variants, including rs2943641 ( Conclusions: While traditional clinical factors remained the strongest predictors of T2D risk, integration of genomic data produced a modest improvement in model performance, especially among younger adults. Validation across independent datasets confirmed the generalizability of these findings, underscoring the value of multi-dimensional risk-prediction models to refine T2D risk assessment.