Performance of machine-learning algorithms to pattern recognition and classification of hearing impairment in Brazilian farmers exposed to pesticide and/or cigarette smoke

Impact of Pesticides on Human Health in the Last Six Years in Brazil

“…Tobacco farmers from southern Brazil exposed to pesticides exhibited signs of central auditory dysfunction characterized by decrements in temporal processing and binaural integration processes/abilities [ 139 , 140 ]. Using meatoscopy, pure tone audiometry, logoaudiometry, high-frequency thresholds, and immittance testing, Tomiazzi et al [ 141 ] demonstrate the direct effect of pesticides on hearing loss in 127 participants, of both sexes, aged between 18 and 39, carried out in Pontal do Paranapanema region, one of the less developed regions of the state of São Paulo.…”

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

“…Abdollahi et al (2018) constructed eight ML models to predict sensorineural hearing loss (SNHL) after chemoradiotherapy, of which five had over 70% accuracies and precisions. 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).…”

“…Another study constructed a large database ( n = 2,420,330) to analyzed the impact of diverse noise to the generation of NIHL using ANN but unraveled the unsatisfactory performance with less than 65% accuracy, which was no better than LR model (Kim et al 2011 ). The accuracies of some algorithms were also investigated in several studies which either tried to predict hearing loss with specific etiologies, such as sudden hearing loss (Bing et al 2018 ; Park et al 2020 ), ototoxic hearing loss (Tomiazzi et al 2019 ) and cochlear dead regions (Chang et al 2019 ), or predict SNHL by specific auditory measures, such as OAE (de Waal et al 2002 ; Liu et al 2020 ; Ziavra et al 2004 ) and ABR (Acır et al 2006 ; Molina et al 2016 ). Similarly, five studies did not evaluate or describe the significance of input to cochlear dead regions (Chang et al 2019 ; de Waal et al 2002 ; Liu et al 2020 ; Tomiazzi et al 2019 ; Ziavra et al 2004 ).…”

“…The accuracies of some algorithms were also investigated in several studies which either tried to predict hearing loss with specific etiologies, such as sudden hearing loss (Bing et al 2018 ; Park et al 2020 ), ototoxic hearing loss (Tomiazzi et al 2019 ) and cochlear dead regions (Chang et al 2019 ), or predict SNHL by specific auditory measures, such as OAE (de Waal et al 2002 ; Liu et al 2020 ; Ziavra et al 2004 ) and ABR (Acır et al 2006 ; Molina et al 2016 ). Similarly, five studies did not evaluate or describe the significance of input to cochlear dead regions (Chang et al 2019 ; de Waal et al 2002 ; Liu et al 2020 ; Tomiazzi et al 2019 ; Ziavra et al 2004 ). Therefore, the validity of the accuracy metric is highly dependent on the transparency of model development and input selection.…”

“…In addition, different studies also applied more than one model to predict other types of hearing loss or hearing-related problems. Three types of algorithms achieved good performance in predicting sudden hearing loss (Bing et al 2018 ; Park et al 2020 ), ototoxic hearing loss (Tomiazzi et al 2019 ) and/or SNHL with specific risk factors (Chang et al 2019 ). Also, several studies successfully applied ANN or SVM to clarify different types of SNHL based on the morphology of OAE (de Waal et al 2002 ; Liu et al 2020 ; Ziavra et al 2004 ) or ABR (Acır et al 2006 ).…”