Hybrid Bat-BP: A New Intelligent tool for Diagnosing Noise-Induced Hearing Loss (NIHL) in Malaysian Industrial Workers Nazri Mohd. Nawi 1, a , M. Z. Rehman 1,b , M. I .Ghazali 2,c , M. N. Yahya 2,d , Abdullah Khan 1,e 1 Faculty of Computer Science and Information Technology (FSKTM), Universiti Tun Hussein Onn Malaysia (UTHM), 86400, Parit Raja, Batu Pahat, Johor, Malaysia. 1 Faculty of Mechanical and Manufacturing Engineering (FKMP), Universiti Tun Hussein Onn Malaysia (UTHM), 86400, Parit Raja, Batu Pahat, Johor, Malaysia. a nazri@uthm.edu.my, b zrehman862060@gmail.com, c imran@uthm.edu.my, d musli@uthm.edu.my, e hi100010@siswa.uthm.edu.my Keywords: Noise-induced hearing loss (NIHL), fuzzy logic, fuzzy expert systems, hybrid Bat-BP algorithm, back-propagation neural network, hearing impairment, noise. Abstract. Noise-Induced Hearing Loss (NIHL) has become a major health threat to the Malaysian industrial workers in the recent era due to exposure to high frequency noise produced by the heavy machines. Recently, many studies have been conducted to diagnose the NIHL in industrial workers but unfortunately they neglected some factors that can play a major role in speeding-up NIHL. In this paper, a new Hybrid Bat-BP algorithm which is based on the trio combination of BAT based metaheuristic optimization, back-propagation neural network, and fuzzy logic is proposed to diagnose NIHL in Malaysian industrial workers. The proposed Hybrid Bat-BP will use heat, body mass index (BMI), diabetes, and smoking along with the century old audiometric variables (i.e. age, frequency, and duration of exposure) to better predict NIHL in Malaysian workers. The results obtained through Hybrid Bat-BP will be able to help us identify and reduce the NIHL rate in the workers with high accuracy. Introduction With the advent of Modern Industrial era in Malaysia, noise is becoming a common part of our life. It is tolerant to an extent at some sound pressure levels (SPL) but it becomes intolerable as the exposure to noise is prolonged or SPL is increased. Noise is experienced mostly by blue-collared employees in the manufacturing, packaging and power plants industries, where the noise usually exceeds the permissible limits of 85 decibels exposure as set by the Factories and Machinery Act 1989 [1-2]. One of the major occupational health problems that an Industrial worker faces today due to noise is Noise-Induced Hearing Loss (NIHL). Noise-Induced Hearing Loss (NIHL) usually occurs due to continuous exposure to the noise levels of 90 plus decibels emitting from the heavy machines. NIHL in early stages is curable but in later stages it becomes permanent and left the person handicapped for the rest of his life [3-5]. Recently, a number of studies have been carried-out to find the significant factors involved in causing NIHL in industrial workers. The recent improvements in the technology especially in Neural Networks has paved a way for researchers to predict various harmful effects of noise on humans such as human work efficiency in noisy environment, noise induced sleep disturbance, speech interference in noisy environment, noise induced annoyance [6- 12]. In an early study carried on NIHL [13], three variables such as age, work duration and noise exposure were selected and Levenberg-Marquardt (LM) model was used for hearing impairment prediction in industrial workers. Later in another study, on tympanic membrane perforation, three factors were identified that directly affect human workers (i.e. noise level, frequency and duration of exposure). It also negated the fact that age; an important factor in permanent hearing loss in older people can play the same effect on the young people [6]. More recently, NIHL in workers is predicted using age, work-duration, and noise exposure as the main factors using Gradient Descent with