Peningkatan Performa Model Gradient Boosting dalam Klasifikasi Stroke Melalui Optimasi Grid Search

This study investigates the effect of hyperparameter optimization with grid search on the XGBoost and LightGBM models in stroke classification. The research results show that parameter optimization significantly improves the performance of both models, especially in accuracy, precision, F1 Score, and ROC-AUC Score. In the XGBoost model, improvements are mainly seen in accuracy and precision, while LightGBM shows even improvements in all evaluation metrics. These findings underscore the importance of hyperparameter optimization in building effective classification models to predict stroke risk more accurately and reliably. These findings may contribute to further understanding of the factors that influence stroke and support more appropriate and effective treatment in clinical practice.