
A study developed machine-learning models that can predict hearing preservation after cochlear implant (CI) surgery using preoperative clinical data. The study found that factors such as a history of meningitis, preoperative low-frequency pure tone average (LFPTA), and preoperative standard pure tone average (SPTA) were positively associated with hearing preservation. On the other hand, sudden hearing loss, noise exposure, aural fullness, abnormal anatomy, and tobacco use were negatively associated with hearing preservation. The random forest algorithm demonstrated the highest performance in predicting outcomes.
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