Clin Exp Otorhinolaryngol.  2020 May;13(2):148-156. 10.21053/ceo.2019.01858.

Machine Learning Models for Predicting Hearing Prognosis in Unilateral Idiopathic Sudden Sensorineural Hearing Loss

• Park KV
• Oh KH
• Jeong YJ
• Rhee J
• Han MS
• Han SW
• Choi J

• KMID: 2500287
• DOI: http://doi.org/10.21053/ceo.2019.01858

Abstract

Objectives
. Prognosticating idiopathic sudden sensorineural hearing loss (ISSNHL) is an important challenge. In our study, a dataset was split into training and test sets and cross-validation was implemented on the training set, thereby determining the hyperparameters for machine learning models with high test accuracy and low bias. The effectiveness of the following five machine learning models for predicting the hearing prognosis in patients with ISSNHL after 1 month of treatment was assessed: adaptive boosting, K-nearest neighbor, multilayer perceptron, random forest (RF), and support vector machine (SVM).
Methods
. The medical records of 523 patients with ISSNHL admitted to Korea University Ansan Hospital between January 2010 and October 2017 were retrospectively reviewed. In this study, we analyzed data from 227 patients (recovery, 106; no recovery, 121) after excluding those with missing data. To determine risk factors, statistical hypothesis tests (e.g., the two-sample t-test for continuous variables and the chi-square test for categorical variables) were conducted to compare patients who did or did not recover. Variables were selected using an RF model depending on two criteria (mean decreases in the Gini index and accuracy).
Results
. The SVM model using selected predictors achieved both the highest accuracy (75.36%) and the highest F-score (0.74) on the test set. The RF model with selected variables demonstrated the second-highest accuracy (73.91%) and F-score (0.74). The RF model with the original variables showed the same accuracy (73.91%) as that of the RF model with selected variables, but a lower F-score (0.73). All the tested models, except RF, demonstrated better performance after variable selection based on RF.
Conclusion
. The SVM model with selected predictors was the best-performing of the tested prediction models. The RF model with selected predictors was the second-best model. Therefore, machine learning models can be used to predict hearing recovery in patients with ISSNHL.

Keyword

Sudden Hearing Loss; Machine Learning; Prognosis; Prediction

Figure

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  Fig. 1. Flowchart detailing patient inclusion and exclusion. ISSNHL, idiopathic sudden sensorineural hearing loss; RF, random forest; SVM, support vector machine; MLP, multilayer perceptron; KNN, K-nearest neighbor; AdaBoost, adaptive boosting.

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  Fig. 2. Variable selection by a random forest (RF) using mean decreases in accuracy and the Gini index, according to which the importance score of each variable was calculated. The top five variables for each criterion, excluding initial hearing by frequency (0.125, 0.25, 0.5, 1, 2, 3, 4, and 8 KHz), were included and applied in the five machine learning models. BUN, blood urea nitrogen; ITDI, intratympanic dexamethasone injection; MI, myocardial infarction; Hb, hemoglobulin; CKD, chronic kidney disease; ESR, erythrocyte sedimentation rate; PT, prothrombin time; Cr, creatinine; aPTT, activated partial thromboplastin time.

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  Fig. 3. K-nearest neighbor.

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  Fig. 4. Random forest algorithm.

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  Fig. 5. Support vector machine. The optimal hyperplane consists of w and b which are the the optimal parameters estimated by data.

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  Fig. 6. Adaptive boosting algorithm. H means the predicted class.

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  Fig. 7. Multilayer perceptron. χ, input variable; h, hidden variable; y, predicted probability of hearing recovery after 1 month of treatment.

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  Fig. 8. Test-set accuracy, F-score, and area under the receiver operating characteristic curve (ROC-AUC) of five machine learning models. The support vector machine (SVM) model with selected predictors achieved both the highest accuracy (75.36%) and highest F-score (0.74) on the test set. The random forest (RF) model with selected variables demonstrated the second-highest accuracy (73.91%) and F-score (0.74). The RF model with the original variables showed the same accuracy (73.91%) as that of the RF model with selected variables, but with a lower F-score (0.73). All the tested models, except RF, demonstrated better performance after variable selection based on RF. KNN, K-nearest neighbor; AdaBoost, adaptive boosting; MLP, multilayer perceptron.

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  Fig. 9. Training set accuracy, F-score, and area under the receiver operating characteristic curve (ROC-AUC) of five machine learning models. The support vector machine (SVM) model with the original predictors, the random forest (RF) model with the original predictors and selected predictors, and the adaptive boosting (AdaBoost) model with selected predictors achieved the highest accuracy (100%) and highest F-score (1.0) on the training set. The K-nearest neighbors (KNN) and AdaBoost models demonstrated better performance after variable selection based on RF. MLP, multilayer perceptron.

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