Development of neurobehavioral deterioration risk prediction model for welder: a proposed study

Paiman, Nuur Azreen and Hariri, Azian and Masood, Ibrahim and Noor, Arma and Yusof, Khairul Hazdi and Abdullah, Samsuri and Idris, Ahmad Fu’ad and Mohd Afandi, Mohd Azizi and Asmuin, Nor Zelawati and Leman, Abdul Mutalib (2018) Development of neurobehavioral deterioration risk prediction model for welder: a proposed study. International Journal of Integrated Engineering, 10 (5). pp. 122-129. ISSN 2229-838X

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Risk prediction model estimate the risk of emerging upcoming outcomes for individual based on several underlying characteristics. In welding process, welders have the high risk to expose with the toxicant element which can harm the neuropsychological of a welder. This proposed study will develop a prediction model on neurobehavioral deterioration risk of welders. In order to get the intensity of heavy metal exposure of the welders, airborne personal monitoring and toenail biomarker test will be carried out. Meanwhile, for the neurotoxicity assessment, the workers will undergo the neurobehavioral core test battery and questionnaire survey to identify the neurobehavioral score level. Detail statistical analysis between both assessment results will be carried out for development of prediction model based on artificial neural network. After validation test, the developed artificial neural network prediction model will be applied to another metal base industry for verification purpose. Length of abstract can be proportional to the length of the article. Through this study, it is expected neurobehavioral risk prediction model on detection on early symptoms of neurobehavioral deterioration will be developed This study contribute to better understanding on the effects of heavy metals exposure, especially to central nervous systems among welders.

Item Type: Article
Uncontrolled Keywords: Artificial Neural Network; Biomarker; Neurobehavioral; Prediction Model; Welding Fume Exposure
Subjects: T Technology > T Technology (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Mechanical and Manufacturing Engineering > Department of Mechanical Engineering
Depositing User: UiTM Student Praktikal
Date Deposited: 25 Jan 2022 01:39
Last Modified: 25 Jan 2022 01:39

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