Classification of sudden deafness prognosis based on deep learning method

Abstract

Objective:This study aimed to develop predictive models for sudden sensorineural hearing loss through deep belief network (DBN) and explore whether the model performances differ when adopting different outcome criteria. Method: 228 potential predictors involving the clinical characteristics, audio logical data, and serological parameters out of 1 220 hospitalized SSHL patients who were admitted from June 2008 to December 2015 were analyzed retrospectively. The hearing data of sudden deafness were classified into two or four categories based on Chinese criteria and Siegel criteria, which were used to develop the DBN models. The area under the receiver operating characteristic curve (ROC-AUC) and accuracy were used to compare the predictive performance of different models. Result: The DBN model developed for predicting the dichotomized outcomes had better performance than that of the fourcategory outcomes. When the iteration number reached 500 times, DBN model constructed for prediction of dichotomized outcomes based on Siegel's criteria had demonstrated the best performance with an accuracy of 76.25% and an AUC of 0.81. According to indices from first layer weights, DBN gave a rank of top 10 sensitive features for hearing outcome prediction focusing on indicators regarding coagulation, demographics and pre-treatment hearing levels independent of the outcome assessment criteria. Conclusion: DBN provides a robust outcome prediction ability in SSHL datasets with rich and complex variables, especially when utilized to predict dichotomized outcomes based on the Siegel criteria. In addition, this advanced deep learning technique can automatically extract valuable predictors, which is consistent with those that had been verified in previous studies by traditional statistical methods. This study provides further evidence for extending the use of DBN algorithm to the field of developing prediction or classification models for other otological diseases in the future..