Akeel Sh. Mahmoud

Abstract Chronic kidney disease (CKD) is a progressive condition affecting over 850 million people worldwide, where timely detection and accurate staging are critical to reducing morbidity, mortality, and healthcare burden. Current machine learning approaches often treat CKD prediction as a binary task, neglecting the ordered nature of disease stages, and rarely incorporate physiological constraints or calibrated probability estimates essential for clinical decision support.