Using audiometric data to weigh and prioritize factors that affect workers hearing loss through Support Vector Machine (SVM)

Background: Workers exposure to excessive noise is a big universal work-related challenge. One of the major consequences of exposure to noise is permanent or transient hearing loss. The current study sought to utilize audiometric data to weigh and prioritize factors affecting workers hearing loss based using the Support Vector Machine (SVM).Materials & Methods: This cross sectional-descriptive study was conducted in 2017 in a mining industry in southeast Iran. The participating workers (n=150) were divided into three groups of 50 based on the sound pressure level to which they were exposed (two experimental groups and one control group). Audiometric tests were carried out for all members of each group. The study generally entailed the following steps: (1) selecting predicting variables to weigh and prioritize factors affecting hearing loss; (2) conducting audiometric tests and assessing permanent hearing loss in each ear and then evaluating total hearing loss; (3) categorizing different types of hearing loss; (4) weighing and prioritizing factors that affect hearing loss based on the SVM; and (5) assessing the error rate and accuracy of the models. The collected data were fed into SPSS 18, followed by conducting linear regression and paired samples t-test.Results: It was revealed that, in the first model (SPL<70 dBA), the frequency of 8 KHz had the greatest impact (with a weight of 33%), while noise had the smallest influence (with a weight of 5%). The accuracy of this model was 100%.

Keywords:

Noise , modeling , hearing loss , data mining , Support Vector Machine Algorithm