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Updated: Jan 17, 2026
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Published on: January 17, 2025
Using Machine Learning to Predict Cochlear Implant Outcomes
Madeleine Anthonisen1, Diane Lazard2,3, Alexandre Lehmann4
1Faculté de médecine et des sciences de la santé, Université de Sherbrooke, Sherbrooke, Québec, Canada, antm5380@usherbrooke.ca.
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 2,251 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 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.
