Predicting the Hearing Outcome in Sudden Sensorineural Hearing Loss via Machine Learning Models

Abstract

Objective

Sudden sensorineural hearing loss (SSHL) is a multifactorial disorder with high heterogeneity, thus the outcomes vary widely. The present study aimed to develop predictive models based on four machine learning methods for SSHL, identifying the best performer for clinical application.

Design

Single-center retrospective study.

Setting

Chinese People's liberation army (PLA) hospital, Beijing, China.

Participants

1220 in-patient SSHL patients were enrolled between June 2008 and December 2015.

Main outcome measures

An advanced deep learning technique, deep belief network (DBN), together with the conventional logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) were developed to predict the dichotomized hearing outcome of SSHL by inputting six feature collections derived from 149 potential predictors. Accuracy, precision, recall, F-score and the area under the receiver operator characteristic curves (ROC-AUC) were exploited to compare the prediction performance of different models.

Results

Overall the best predictive ability was provided by the DBN model when tested in the raw dataset with 149 variables, achieving an accuracy of 77.58% and AUC of 0.84. Nevertheless DBN yielded inferior performance after feature pruning. In contrast, the LR, SVM and MLP models demonstrated opposite trend as the greatest individual prediction powers were obtained when included merely three variables, with the ROC-AUC ranging from 0.79 to 0.81, and then decreased with the increasing size of input features combinations.

Conclusions

With the input of enough features, DBN can be a robust prediction tool for SSHL. But LR is more practical for early prediction in routine clinical application by using three readily available variables, i.e. time elapse between symptom onset and study entry, initial hearing level and audiogram.

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from #ORL-AlexandrosSfakianakis via ola Kala on Inoreader http://ift.tt/2n1mEKL