Factors Influencing Hearing Preservation in Cochlear Implant Patients: A Predictive Modelling Approach.

Abstract

Introduction: Hearing loss, affecting over 19% of the global population, is a major disability worldwide, with its prevalence expected to increase due to demographic changes. Cochlear implants (CIs) provide a crucial treatment for severe to profound sensorineural hearing loss when conventional hearing aids fail. Although technological and surgical advancements have expanded CI indications, hearing preservation (HP) after implantation remains unpredictable and varies significantly among patients. Recent studies indicate that machine learning (ML) methods could offer improved prediction. Therefore, this study aimed to evaluate the feasibility of predicting HP in potential CI users.

Methods: Clinical data from 225 CI patients (mean age: 59.9 years) implanted at Hannover Medical School (MHH) between 2009 and 2024 were retrospectively analyzed. ML models were developed and compared with baseline models such as linear regression and a mean predictor.

Results: Among all models, the Random Forest (RF) achieved the best predictive performance. Electrode insertion angle and age at implantation were identified as the most influential features for predicting HP, contributing 61.0% and 24.3% respectively. Despite the results of the RF model, limitations such as prediction error and a small dataset were acknowledged.

Conclusion: The study highlights the potential of ML methods for predicting HP in CI users but underscores the need for the integration of more surgical and objective data.