Enhanced Diabetes Risk Prediction Using Ensemble Learning on Real-World Nhanes Clinical Data

In this study, we explore a machine literacy approach for forecasting early-stage diabetes using real clinical data gathered from NHANES (National Health and Nutrition Examination Survey). The dataset includes crucial medical pointers similar as glucose, insulin, BMI, blood pressure, and cholesterol. Two ensemble classifiers Random Forest and XGBoost were trained and estimated. The Random Forest model achieved the stylish performance, with 94% delicacy and an AUC of 0.96. Our engineered features, such as the Glucose-to-Insulin Ratio and MAP, helped our model better distinguish diabetic individuals. Our findings highlight how ensemble learning can be effectively used in real-world settings for interpretable clinical screening.