A machine learning-based framework for predicting type 2 diabetes mellitus using hematological indices.

Objective: This study aimed to develop a machine learning (ML) framework to predict incident type 2 diabetes mellitus (T2DM) using routinely available hematological and renal biomarkers, and to assess their added predictive value over conventional clinical risk factors. Methods: We analyzed data from 6093 diabetes-free participants from the prospective Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) cohort. Predictors included white blood cell count (WBC), red blood cell count (RBC), red cell distribution width (RDW), and other hematological/renal factors. We employed logistic regression and multiple ML models (Random Forest, XGBoost, LightGBM), optimized via grid search and cross-validation. Results: Multivariate logistic regression identified RBC, WBC, and RDW as independent predictors of T2DM. The Random Forest model achieved the highest performance with a ROC-AUC of 0.73, an accuracy of 0.67, and correctly identified 223 of 347 incident T2DM cases Using a probability threshold of 0.20, the Random Forest model achieved a sensitivity of 0.618, a specificity of 0.707, a positive predictive value (PPV) of 0.327, and a negative predictive value (NPV) of 0.889 on the independent test set Feature importance analysis identified metabolic syndrome, BMI, uric acid, and age as the strongest contributors, while WBC, RBC, and NLR were the most influential hematological predictors. Conclusion: Routine hematological indices, including RBC, WBC, and RDW, were independently associated with incident T2DM, while metabolic syndrome, BMI, uric acid, and age contributed most strongly to overall model prediction. ML provides a complementary approach for early risk stratification, although further validation is required before clinical implementation.