Development of noise induced hearing loss prediction model using Levenberg Marquardt / Siti Fairus Mohd Zain … [et al.] - Mohd Zain, Siti Fairus; Sulaiman, Ahamad Asari; Yasin, Siti Munira; Zamhuri, Mohammad Idris; Abdullah Hair, Ahmad Fitri; Hussin, Mohamad Fahmi; Ismail, Ismaniza (UiTM Press, 2023)
This research explores the creation of a noise-induced hearing loss (NIHL) prediction model. In the context of occupational health, NIHL is defined as hearing loss that occurs as a result of overexposure to noise hazards at work for a given noise level and time. Despite the high statistical instances that have been recorded over the course of a number of years, there have been very few, if any, efforts made to construct an appropriate prediction model using the combination of linked diagnostic occupational hazards. This study aims to show how an Artificial Neural Network Levenberg-Marquardt algorithm can be used to make a prediction model for NIHL. The goal is to find highly linked risk factors that increase the number of NIHL cases reported in Selangor, Malaysia. The study looked at 355 secondary data points taken from NIHL confirmed cases and given by the Department of Occupational Safety and Health (DOSH). The overall performance of the ANN prediction model was tested at a level of 90.46 percent average. Because of the great accuracy in predicting NIHL, it is inferred that the model may be employed as an intelligent system in the preliminary screening phase.