Possibilistic regression analysis of influential factors for occupational health and safety management systems

Azizul Azhar Ramli, Junzo Watada, Witold Pedrycz

    Research output: Contribution to journalArticle

    19 Citations (Scopus)

    Abstract

    The code of occupational health and safety (OHS) is an influential regulation to improve the on-the-job safety of employees. A number of factors influence the planning and implementation of OHS management systems (OHSMS). The evaluation of OHSMS practice is the most important component when forming a health and safety environmental policy for employees. The objective of this research is to develop an intelligent data analysis (IDA) in which possibilistic regression being endowed with a convex hull approach is used to support the analysis of essential factors that influence OHSMS. Given such subjective terms, the obtained samples can be conveniently regarded as fuzzy input/output data represented by membership functions. The study offers this vehicle of intelligent data analysis as an alternative to evaluate the influential factors in a successful implementation of OHS policies and in this way decrease an overall computational effort. The obtained results show that several related OHSMS influential factors need to be carefully considered to facilitate a successful implementation of the OHSMS procedure.

    Original languageEnglish
    Pages (from-to)1110-1117
    Number of pages8
    JournalSafety Science
    Volume49
    Issue number8-9
    DOIs
    Publication statusPublished - 2011 Oct

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    Keywords

    • Intelligent data analysis
    • Occupational health and safety management systems
    • Possibilistic regression analysis

    ASJC Scopus subject areas

    • Safety Research
    • Public Health, Environmental and Occupational Health
    • Safety, Risk, Reliability and Quality

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