Empirical estimation of the grades of hearing impairment among industrial workers based on new artificial neural networks and classical regression methods

Abstract

Background:Prediction models are used in a variety of medical domains, and they are frequently built from experience which constitutes data acquired from actual cases. This study aimed to analyze the potential of artificial neural networks and logistic regression techniques for estimation of hearing impairment among industrial workers.Materials and Methods:A total of 210 workers employed in a steel factory (in West of Iran) were selected, and their occupational exposure histories were analyzed. The hearing loss thresholds of the studied workers were determined using a calibrated audiometer. The personal noise exposures were also measured using a noise dosimeter in the workstations. Data obtained from five variables, which can influence the hearing loss, were used as input features, and the hearing loss thresholds were considered as target feature of the prediction methods. Multilayer feedforward neural networks and logistic regression were developed using MATLAB R2011a software.Results:Based on the World Health Organization classification for the grades of hearing loss, 74.2% of the studied workers have normal hearing thresholds, 23.4% have slight hearing loss, and 2.4% have moderate hearing loss. The accuracy and kappa coefficient of the best developed neural networks for prediction of the grades of hearing loss were 88.6 and 66.30, respectively. The accuracy and kappa coefficient of the logistic regression were also 84.28 and 51.30, respectively.Conclusion:Neural networks could provide more accurate predictions of the hearing loss than logistic regression. The prediction method can provide reliable and comprehensible information for occupational health and medicine experts.

Contributions and limitations of using machine learning to predict noise-induced hearing loss

“…Other studies showed similar high accuracies with ML models used to predict sudden sensorineural hearing loss (SSNHL) and otoxic-induced hearing loss (Bing et al 2018;Tomiazzi et al 2019). Varied accuracies between 64 and 99% were reported by different studies using different ML algorithms and inputs to predict risk factors for NIHL (Aliabadi et al 2015;Farhadian et al 2015;Kim et al 2011;Mohd Nawi et al 2011;Zhao et al 2019a).…”

“…It is noteworthy, however, that not all ML algorithms are substantially superior to traditional statistical regression analysis in terms of model performance when predicting hearing loss caused by specific risk factors (Abdollahi et al 2018;Bing et al 2018;Farhadian et al 2015). To the best of our knowledge, there is no literature review evaluating the quality of ML models to predict NIHL.…”

Dental age estimation using the pulp-to-tooth ratio in canines by neural networks

“…If an improper nonlinear regression function is selected, it may represent a nonlinear relationship with less precision than its linear counterpart. In the situation described above, where the relationship between the output and input is nonlinear and the form of nonlinearity is not specified, an self-adjusting approach with more flexibility that can accommodate various types of nonlinear behavior, including a wide class of nonlinear mappings, is required 141516. Neural networks are well-suited for a very broad class of nonlinear approximations and mappings.…”

“…Neural networks are well-suited for a very broad class of nonlinear approximations and mappings. As important standard machine learning procedures for classification and regression, neural networks have recently become widespread in many disciplines, including biology and medicine 141516. Of particular note, feed-forward neural networks are nonparametric statistical models for deriving nonlinear relationships among data 14…”

“…As a result, neural networks could represent another option for solving complex prediction problems. They are increasingly become popular in medicine, and are especially successful in modeling nonlinear relationships between the predicted variable and the input data 1516. Indeed, applications of neural networks in other domains have shown this approach to perform better than regression models.…”