Using Machine Learning to Predict Cochlear Implant Outcomes

Introduction: Cochlear implant outcomes vary widely and are difficult to predict, with traditional methods explaining <20% of variance. This study tested whether machine learning approaches offer superior performance predicting outcomes and better identify key factors driving variability compared to traditional linear methods. Methods: This retrospective observational study analyzed clinical data from 2251 adult cochlear implant recipients (>18 years) with post-lingual hearing loss (onset >15 years) across fifteen centers in Australia, Europe, and North America. Data were collected between 2003 and 2011, with follow-up at 6 months and 2 years post-implantation. Linear Regression was compared against seven other machine learning models: Extreme Gradient Boosting (XGBoost), Random Forest, Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), Support Vector Regression, Ridge Regression, and Lasso Regression. Models were optimized using grid search with 5-fold cross-validation on an 80/20 training-test split. The primary outcome was prediction of percentile-ranked postoperative speech recognition scores in quiet, assessed using mean squared error (MSE) and coefficient of determination (R²). SHapley Additive exPlanations (SHAP) values identified feature importance. Results: XGBoost achieved the best performance with a modest but significant 4.11% reduction in prediction error compared to linear regression (mean squared error: 739.67±19.27 vs 771.41±21.51, P = 0.003; R²: 0.114±0.011 vs 0.076±0.012, P < 0.001). All ensemble methods significantly outperformed linear regression. Duration of cochlear implant use, age at implantation, duration of severe/profound hearing loss, and preoperative hearing scores emerged as the most influential predictors across all models. Conclusions: Machine learning models modestly improve cochlear implant outcome prediction, though substantial variance remains unexplained (>80%). Critical determinants of cochlear implant performance likely extend beyond variables routinely measured in clinical practice, highlighting the need for novel predictive factors.