Development of a machine learning model for prognostic prediction of severe sudden sensorineural hearing loss with hyperbaric oxygen therapy

Background: Severe sudden sensorineural hearing loss (SSNHL) has heterogeneous causes and variable outcomes, making individualized prognosis difficult. We aimed to develop and evaluate a machine-learning (ML) model to predict recovery in severe SSNHL while treating hyperbaric oxygen therapy (HBOT) as an exposure feature rather than inferring causal treatment effects. Methods: In a single-center retrospective cohort, we analyzed clinical and audiometric data from 231 in patients with severe SSNHL treated between January 2015 and January 2024. Recovery was defined by Siegel's criteria; eligibility required ≥70 dB loss and treatment initiation within 1 month of onset. Candidate predictors included demographics, comorbidities, baseline thresholds, time to treatment, and HBOT variables (e.g., session count). We trained a custom multilayer perceptron with 12 input features and compared it with conventional algorithms. Performance was assessed using accuracy, F1 score, precision, recall, and area under the ROC curve (AUC). Results: Among 231 patients, the custom model achieved 89.36% test accuracy and an AUC of 0.8716, outperforming several conventional methods. Key predictors included age, diabetes, dizziness, and HBOT exposure. Notably, "≥10 HBOT sessions" showed high importance in logistic regression and SVM models, suggesting prognostic relevance of sufficient cumulative HBOT exposure. Conclusion: Including HBOT information as a feature improved prediction of recovery in severe SSNHL; however, these findings do not establish the therapeutic efficacy of HBOT. The model may support clinician-patient decision-making by providing individualized recovery probabilities. Limitations include the retrospective single-center design, modest sample size, and class imbalance, underscoring the need for external validation and better adjustment for confounding.