Decoding the renal–cochlear axis: explainable machine learning and phenotype clustering reveal high-risk hearing loss subtypes in CKD

= 2,316) characterized by older age, elevated blood urea nitrogen and bicarbonate levels, and a 48.2% HL prevalence. A classifier trained on these subtypes achieved discrimination (AUC = 0.99974). A clinically a web-based tool was also developed based on the six most influential features. The findings establish a dual-level predictive framework integrating explainable ML and unsupervised clustering for HL risk assessment in CKD. This approach provides a robust strategy for the precision screening of high-risk subpopulations and supports the integration of hearing assessments into routine CKD.