Predicting Postoperative Cochlear Implant Performance Using Supervised Machine Learning.

ABSTRACT

OBJECTIVES: To predict postoperative cochlear implant performance with heterogeneous text and numerical variables using supervised machine learning techniques. STUDY DESIGN: A supervised machine learning approach comprising neural networks and decision tree-based ensemble algorithms were used to predict 1-year postoperative cochlear implant performance based on retrospective data. SETTING: Tertiary referral center. PATIENTS One thousand six hundred four adults who received one cochlear implant from 1989 to 2019. Two hundred eighty two text and numerical objective demographic, audiometric, and patient-reported outcome survey instrument variables were included. OUTCOME MEASURES: Outcomes for postoperative cochlear implant performance were discrete Hearing in Noise Test (HINT; %) performance and binned HINT performance classification ("High," "Mid," and "Low" performers). Algorithm performance was assessed using hold-out validation datasets and were compared using root mean square error (RMSE) in the units of the target variable and classification accuracy. RESULTS: The neural network 1-year HINT prediction RMSE and classification accuracy were 0.57 and 95.4%, respectively, with only numerical variable inputs. Using both text and numerical variables, neural networks predicted postoperative HINT with a RMSE of 25.0%, and classification accuracy of 73.3%. When applied to numerical variables only, the XGBoost algorithm produced a 1-year HINT score prediction performance RMSE of 25.3%. We identified over 20 influential variables including preoperative sentence-test performance, age at surgery, as well as specific tinnitus handicap inventory (THI), Short Form 36 (SF-36), and health utilities index (HUI) question responses as the highest influencers of postoperative HINT. CONCLUSION: Our results suggest that supervised machine learning can predict postoperative cochlear implant performance and identify preoperative factors that significantly influence that performance. These algorithms can help improve the understanding of the diverse factors that impact functional performance from heterogeneous data sources.