Machine Learning Versus Simple Clinical Models for Cochlear Implant Outcome Prediction.

ABSTRACT: Background/Objectives: Cochlear implantation is the most widely used treatment option for patients with severe to profound hearing loss. Despite being a relatively standardized surgical procedure, cochlear implant (CI) outcomes vary considerably among patients. Several studies have attempted to develop predictive models for CI outcomes but achieving accurate and generalizable predictions remains challenging. The present study aimed to evaluate whether simple and complex statistical and machine learning models could outperform the Null model based on various pre-CI implantation variables. Methods: We conducted a retrospective analysis of 236 ears with postlingual profound sensorineural hearing loss (SNHL) and measurable residual hearing (WRS_max > 0%) at the time of

SUBMITTER: Ollermann R 

PROVIDER: S-EPMC12641834 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature