Machine learning based assessment of auditory threshold perception in human beings

• Published in: SN applied sciences Vol. 2; no. 2; p. 147
• Main Authors: Sanjay, H. S., Hiremath, Basavaraj V., Prithvi, B. S., Dinesh, P. A.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.02.2020; Springer Nature B.V
• Subjects: 6. Interdisciplinary (general); Applied and Technical Physics; Artificial intelligence; Auditory discrimination learning; Chemical industry; Chemicals; Chemistry/Food Science; Classification; Data analysis; Decision making; Earth Sciences; Employees; Engineering; Environment; Error functions; Exposure; Fabrication; Hearing; Hearing loss; Kernel functions; Learning algorithms; Machine learning; Materials Science; Neural networks; Noise; Occupational exposure; Occupational health; Perception; Rehabilitation; Research Article; Solvents; Sound; Sports; Support vector machines; Training; Hearing perception; Radial basis kernel function; Absolute threshold test; k-Nearest neighbor; Support vector machine; Multi-layer perceptron; Levenberg–Marquardt
• ISSN: 2523-3963; 2523-3971
• DOI: 10.1007/s42452-019-1929-7

This study presents a novel approach to assess the perception of auditory Absolute threshold (ATTh) in healthy individuals exposed to noise and solvents in their occupational environment using machine learning approaches. 396 subjects with no known history of auditory pathology were chosen from three groups, namely, employees from Chemical Industries (CI), Fabrication Industries (FI), and professional Basketball Players (BP), with each category having 132 subjects. Absolute Threshold Test (ATT) was developed using MATLAB and the experiment was conducted in a silent, noise-free environment. ATTh was obtained twice, during the commencement and conclusion of the employees’ workshift in CI and FI. For BP, ATTh was obtained before and after their basketball training sessions and was used as features for binary SVM classification approach, in which the RBF kernel-based technique was found to provide maximum accuracy as compared to linear and quadratic approach. For three-class classification, MLP neural network with Levenberg–Marquardt training function in the hidden layer and Mean Square Error function in the output layer was found to be optimal along with k-Nearest Neighbor (kNN) and Support Vector Machine (SVM) approach using Radial Basis Kernel Function (RBF), in which, an accuracy of 81.06% was observed in kNN approach and 92.4% using MLP neural network approach, whereas SVM yielded an accuracy of 93.94% in the classification of the subjects into CI, FI and BP, showing that the SVM outperformed kNN and MLP neural network for healthy subjects based on their occupational exposure/professional sports training. Such machine learning approaches could further be probed into, to improve the accuracy of classification. Also, such techniques can help in real-time classification of subjects based on their occupational exposure so as to predict and prevent plausible permanent hearing dysfunction due to occupational exposure as well as to aid in sports rehabilitation and training programs to assess the auditory perceptive abilities of the individuals.