01618nas a2200205   4500000000100000008004100001653002700042653001900069653001800088653002400106653002400130653002200154653002100176100002100197700002100218245007600239300001300315490000700328520107700335       2024                            d10achronic kidney disease10aCKD prediction10abat algorithm10agrey wolf optimizer10ahybrid optimization10afeature selection10amachine learning1 aWaheeda Almayyan1 aBareeq AlGhannam00aA Hybrid Bat-Grey Wolf Algorithm for Predicting Chronic Kidney Disease   a199-212 0 v553 a<p>Chronic Kidney Disease (CKD) is a significant global health concern characterized by major global health issues defined by progressive kidney damage. Early detection is essential for chronic kidney disease (CKD) as it often progresses silently and can lead to severe complications. This study introduces a novel hybrid optimization algorithm, combining the Bat Algorithm (BA) and Grey Wolf Optimizer (GWO), to address the challenges of CKD prediction. By leveraging BA’s exploration capabilities and GWO’s exploitation potential, the proposed algorithm effectively searches for optimal solutions, enhancing the accuracy and efficiency of CKD diagnosis. The proposed algorithm, termed BAGWO, iteratively refines solutions through a two-stage process, enhancing the model’s ability to converge on accurate and informative results. This algorithm not only enhanced classification accuracy but also reduced the feature set to only six attributes. The Random Forest classifier resulted in a recognition rate of 99.5&nbsp;%, making it the top-performing classifier.</p>