Building the LightGBM Model for Early Diabetes Detection

In this post, we cover the full modeling workflow for the Diabetes Detection App. We begin by cleaning and preparing the dataset, handling missing values, encoding categorical variables, and exploring feature correlations. The LightGBM algorithm was selected for its speed, handling of categorical variables, and strong performance in imbalanced classification tasks. We walk through hyperparameter tuning, cross-validation, scoring methods, and evaluation metrics. The final optimized model was deployed as part of the FastAPI backend and now powers the mobile app’s risk prediction engine.