<![CDATA[Chronic kidney disease is an increasingly prevalent health issue that requires more precise clinical data-based early detection methods to enable timely and appropriate treatment. This study focuses on developing a predictive model for chronic kidney disease using the Light Gradient Boosting Machine (LightGBM) algorithm and enhancing its performance through hyperparameter optimization with the Grey Wolf Optimizer (GWO). The dataset used originates from public sources and undergoes several preprocessing steps, including missing value imputation, categorical feature encoding, outlier handling, initial feature selection, and stratified data splitting to maintain model quality. Three modeling approaches were evaluated: LightGBM with default parameters, LightGBM enhanced using Random Search, and LightGBM optimized with GWO. The experimental results indicate that the baseline model already performs well, Random Search improves accuracy and F1-score, and GWO achieves the highest AUC-ROC value despite requiring longer computation time. Significance testing through cross-validation shows that the performance differences among the three models are not statistically significant, suggesting that the observed improvements are not strong enough to determine a definitively superior optimization method. The feature importance analysis highlights that clinical indicators such as creatinine levels, glomerular filtration rate, blood pressure, and urine protein contribute most prominently to the prediction. Overall, the study demonstrates that LightGBM is a reliable model for early detection of chronic kidney disease, and hyperparameter optimization still offers added value that can support the development of AI-based clinical decision-support systems]]>
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