Abstract
Hearing Wellness is an Artificial Intelligence (AI)-powered mobile application poised to revolutionize how we approach Noise Induced Hearing Loss (NIHL) in the workplace. By harnessing the predictive prowess of Support Vector Machine (SVM) technology, this innovative application predicts the NIHL level among workers with the symptoms, to prevent the spread of this disease in the workplace. The mobile application system serves as predictive modelling system of NIHL and also aims to assist Occupational Health Doctors (OHD) in upgrading the current system from the conventional manual data keeping to digital Drawing insights from real-world NIHL cases sourced from the Department of Occupational Safety and Health Selangor (DOSH), Hearing Wellness empowers workers and Occupational Health Doctors (OHD) alike with a cutting-edge predictive modeling system. This shift from traditional manual record-keeping to seamless digital data management not only streamlines processes but also enhances efficiency in monitoring and addressing NIHL concerns. By facilitating swift intervention and fostering a safer, healthier work environment, this transformative tool is set to redefine workplace safety standards and lead the charge in mitigating the impact of NIHL.
Metadata
| Item Type: | Book Section |
|---|---|
| Creators: | Creators Email / ID Num. Mohd Zain, Siti Fairus UNSPECIFIED Hussin, Mohamad Fahmi UNSPECIFIED Aziz, Azri UNSPECIFIED Sulaiman, Ahmad Asari asari100@uitm.edu.my Ismail, Ismaniza ismaniza@uitm.edu.my |
| Contributors: | Contribution Name Email / ID Num. Editor Mohd Zukhi, Mohd Zhafri zhafri319@uitm.edu.my Editor Zakaria, Shahida Farhan shahidafarhan@uitm.edu.my Editor Shamsuddin, Norin Rahayu norinrahayu@uitm.edu.my |
| Subjects: | T Technology > T Technology (General) > Technological change T Technology > T Technology (General) > Technological change > Technological innovations |
| Divisions: | Universiti Teknologi MARA, Kedah > Sg Petani Campus |
| Page Range: | p. 74 |
| Keywords: | Noise induced hearing loss, Support vector machine, Prediction model, Artificial intelligence |
| Date: | 2024 |
| URI: | https://ir.uitm.edu.my/id/eprint/143000 |
