# Explainable machine learning model for predicting hearing recovery in unilateral sudden sensorineural hearing loss

## TL;DR

This study developed an explainable machine learning model to predict hearing recovery in sudden sensorineural hearing loss patients, using features like hearing loss degree and MCV.

## Contribution

The study introduces MCV as a pivotal factor for hearing recovery in SSNHL and uses SHAP to explain ML model features for clinicians.

## Key findings

- The Random Forest model achieved an AUC of 0.998 in predicting hearing recovery.
- Degree of hearing loss and MCV were identified as key predictors.
- SHAP method enhanced clinical understanding of ML model features.

## Abstract

•Explainable ML performed well in predicting hearing recovery in SSNHL patients.•The RF model performed best among the ML models•The most primary feature was degree of hearing loss.•MCV was found to be an pivotal factor for the first time.•The SHAP method help clinicians to better understand the features in ML models.

Explainable ML performed well in predicting hearing recovery in SSNHL patients.

The RF model performed best among the ML models

The most primary feature was degree of hearing loss.

MCV was found to be an pivotal factor for the first time.

The SHAP method help clinicians to better understand the features in ML models.

Sudden Sensorineural Hearing Loss (SSNHL) is routinely encountered in otolaryngology clinics. The prognosis of SSNHL varies dramatically and depends on multiple influence factors. This study aimed to develop an explainable Machine Learning (ML) model with easily accessible features to predict the prognosis of SSNHL.

This bi-center retrospective study included 534 patients with SSNHL. We randomly split the data into training and validation sets. Univariate analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression were employed to select predominant features, including demographic, disease-specific characteristics, and laboratory items. We evaluated the performance of five ML models constructed with six crucial variables using the Area Under the receiver operating characteristic Curve (AUC), accuracy, specificity, sensitivity, and F1 scores. These models were further calibrated by calibration curve and Brier score. Clinical utility was evaluated by Decision Curve Analysis (DCA). The Shapley Additive Explanations (SHAP) method was applied to interpret feature contribution and explain the ML models.

The Random Forest (RF) model reached the highest AUC of 0.998. Its accuracy, sensitivity, specificity, and F1 score were 0.981, 0.963, 0.989, and 0.967, respectively. DCA curve analysis revealed a comparable net benefit of the models. The SHAP method revealed that the primary features were degree of hearing loss, audiogram type, age, Mean Corpuscular Volume (MCV), onset to treatment, and serum Albumin (ALB) accordingly.

The explainable ML model was superb in predicting hearing outcome and providing information on feature contributions. Clinicians can better understand the contributors to hearing recovery and guide aural rehabilitation using the SHAP method.

Level 3.