Abstract
Despite the significance of predicting the prognosis of idiopathic sudden sensorineural hearing loss (ISSNHL), no predictive models have been established. This study used artificial intelligence to develop prognosis models to predict recovery from ISSNHL. We retrospectively reviewed the medical data of 453 patients with ISSNHL (men, 220; women, 233; mean age, 50.3 years) who underwent treatment at a tertiary hospital between January 2021 and December 2019 and were followed up after 1 month. According to Siegel’s criteria, 203 patients recovered in 1 month. Demographic characteristics, clinical and laboratory data, and pure-tone audiometry were analyzed. Logistic regression (baseline), a support vector machine, extreme gradient boosting, a light gradient boosting machine, and multilayer perceptron were used. The outcomes were the area under the receiver operating characteristic curve (AUROC) primarily, area under the precision-recall curve, Brier score, balanced accuracy, and F1 score. The light gradient boosting machine model had the best AUROC and balanced accuracy. Together with multilayer perceptron, it was also significantly superior to logistic regression in terms of AUROC. Using the SHapley Additive exPlanation method, we found that the initial audiogram shape is the most important prognostic factor. Machine/deep learning methods were successfully established to predict the prognosis of ISSNHL.
Highlights
Despite the significance of predicting the prognosis of idiopathic sudden sensorineural hearing loss (ISSNHL), no predictive models have been established
Predictive models based on four machine learning methods including deep belief network (DBN), logistic regression (LR), support vector machine (SVM), and multilayer perceptron (MLP) have been applied in ISSNHL with the outcomes of 1220 patients[13]
In our previous study, machine learning methods including adaptive boosting, K-nearest neighbor, MLP, random forest (RF), and SVM were used with the data from 227 patients[15]
Summary
Despite the significance of predicting the prognosis of idiopathic sudden sensorineural hearing loss (ISSNHL), no predictive models have been established. Due to the unpredictable course of ISSNHL, several variables that appear to influence the prognosis of ISSNHL have been identified These include the severity of hearing loss, audiogram shape, presence of vertigo, and a ge[2,3,5–9]. Creating optimization models to predict a prognosis by analyzing various factors using artificial intelligence as well as selecting important variables can be an innovative method in any medical field. Predictive models based on four machine learning methods including deep belief network (DBN), logistic regression (LR), support vector machine (SVM), and multilayer perceptron (MLP) have been applied in ISSNHL with the outcomes of 1220 patients[13]. This study aimed to assess new important variables and increase the performance of machine learning/deep learning models for predicting hearing recovery in patients with ISSNHL after 1 month of treatment
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
