Using Machine Learning to Predict Cochlear Implant Outcomes

INTRODUCTION: Cochlear implant outcomes vary widely and are difficult to predict, with traditional methods explaining 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 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 (MSE: 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.