Abstract
Objective
This study aimed to maximise the ability of stimulus-frequency otoacoustic emissions (SFOAEs) to predict hearing status and thresholds based on machine-learning models.
Design
SFOAE data and audiometric thresholds were collected at octave frequencies from 0.5 to 8 kHz. Support vector machine, k-nearest neighbour, back propagation neural network, decision tree, and random forest algorithms were used to build classification models for status identification and to develop regression models for threshold prediction.
Study sample
About 230 ears with normal hearing and 737 ears with sensorineural hearing loss.
Results
All classification models yielded areas under the receiver operating characteristic curve of 0.926–0.994 at 0.5–8 kHz, superior to the previous SFOAE study. The regression models produced lower standard errors (8.1–12.2 dB, mean absolute errors: 5.53–8.97 dB) as compared to those for distortion-product and transient-evoked otoacoustic emissions previously reported (8.6–19.2 dB).
Conclusions
SFOAEs using machine-learning approaches offer promising tools for the prediction of hearing capabilities, at least at 0.5–4 kHz. Future research may focus on further improvements in accuracy and reductions in test time to improve clinical utility.
Acknowledgements
The authors thank Professor Mario Ruggero (Northwestern University) for help with text editing and Fei Ji (The General Hospital of the People’s Liberation Army) for help with data collection.
Disclosure statement
The authors have no conflicts of interest to disclose.