Hybridized light gradient boosting and whale optimization algorithm for diabetes detection

Due to their remarkable precision and effectiveness, gradient-boosted tree models have become the go-to choice for machine learning-driven diabetes detection; however, the key to unlocking their full potential lies significantly in the careful tuning of hyperparameters. To automatically optimize LGBM's hyperparameters for improved diabetes screening, we present a hybrid framework - Light Gradient Boosting (LGBM) bundled with the Whale Optimization Algorithm (LGBM+WOA). Inspired by nature, the Whale Optimization Algorithm (WOA) models the bubble-net feeding behaviour of humpback whales, therefore offering a compromise between exploration and exploitation in search areas. We evaluated model performance under imbalanced class situations using stratified 10-fold cross-validation using the Diabetes Dataset from patients in Borno hospital. Rising above baseline Gradient Boosting (80%), Support Vector Machine (74%), Random Forest (86%), and LGBM (88%), the suggested LGBM+WOA model achieved an overall detection accuracy of 90%. While diabetes recall increased to 0.86, so lowering false negatives is important; class-specific metrics for the non-diabetic cohort obtained a precision of 0.93, recall of 0.91, and F1-score of 0.92 - gains of 1-2 percentage points over standard LGBM. Faster convergence and better generalization follow from WOA-driven hyperparameter tuning, refining important LGBM parameters more effectively than grid or random search. The easier training and testing process of the hybrid model is a helpful tool for quickly assessing diabetes risk and allows for immediate use in clinical decision support systems. Combining LGBM's gradient-boosting efficiency with WOA's robust global optimization, the LGBM+WOA framework provides a new benchmark for machine-learning-based diabetes detection, enabling more general uses of metaheuristic-tuned ensembles in medical diagnostics.