An informed monotone neural network for multi-stage chronic kidney disease prediction with integrated GFR estimation

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. We propose a multi-stage CKD prediction framework that integrates ordinal classification, probability calibration, and a serum creatinine-monotonicity constraint within a knowledge distillation paradigm. A calibrated CatBoost model serves as a teacher, transferring temperature-scaled class probabilities to an ordinal neural network student augmented with an auxiliary eGFR regression task. Evaluated on a cohort of 750 patients from Al-Ramadi Teaching Hospital, our approach achieved superior macro-F1 and reduced expected calibration error. This work demonstrates that combining ordinal learning, calibration, and physiological constraints yields models that are not only accurate but also aligned with clinical reasoning, offering a pathway toward safer and more trustworthy AI tools for CKD management. Limitations like circular reasoning and target leakage are also discussed.