Machine Learning Models for the Hearing Impairment Prediction in Workers Exposed to Complex Industrial Noise: A Pilot Study. Issue 3 (May 2019)

• Record Type: Journal Article
• Authors: Zhao, Yanxia; Li, Jingsong; Zhang, Meibian; Lu, Yao; Xie, Hongwei; Tian, Yu; Qiu, Wei
• Is Part Of: Ear and hearing. Volume 40:Issue 3(2019)
• Publication Date: 2019-05
• Subjects: Complex noise exposure -- Hearing impairment -- Machine learning -- Noise-induced hearing loss; Hearing disorders -- Periodicals; Audiology -- Periodicals; 612.85
• DOI: 10.1097/AUD.0000000000000649
• Languages: English
• ISSNs: 0196-0202

Abstract

Abstract : Objectives: To demonstrate the feasibility of developing machine learning models for the prediction of hearing impairment in humans exposed to complex non-Gaussian industrial noise. Design: Audiometric and noise exposure data were collected on a population of screened workers (N = 1, 113) from 17 factories located in Zhejiang province, China. All the subjects were exposed to complex noise. Each subject was given an otologic examination to determine their pure-tone hearing threshold levels and had their personal full-shift noise recorded. For each subject, the hearing loss was evaluated according to the hearing impairment definition of the National Institute for Occupational Safety and Health. Age, exposure duration, equivalent A-weighted SPL (LAeq ), and median kurtosis were used as the input for four machine learning algorithms, that is, support vector machine, neural network multilayer perceptron, random forest, and adaptive boosting. Both classification and regression models were developed to predict noise-induced hearing loss applying these four machine learning algorithms. Two indexes, area under the curve and prediction accuracy, were used to assess the performances of the classification models for predicting hearing impairment of workers. Root mean square error was used to quantify the prediction performance of the regression models. Results: A prediction accuracy between 78.6 and 80.1% indicated that the four classification models could be useful tools to assess noise-induced hearing impairment of workers exposed to various complex occupational noises. A comprehensive evaluation using both the area under the curve and prediction accuracy showed that the support vector machine model achieved the best score and thus should be selected as the tool with the highest potential for predicting hearing impairment from the occupational noise exposures in this study. The root mean square error performance indicated that the four regression models could be used to predict noise-induced hearing loss quantitatively and the multilayer perceptron regression model had the best performance. Conclusions: This pilot study demonstrated that machine learning algorithms are potential tools for the evaluation and prediction of noise-induced hearing impairment in workers exposed to diverse complex industrial noises.