Application of rough set theory in accident analysis at work: A case study

Abstract

Though accident data have been collected across industries, they may inherently contain uncertainty of randomness and fuzziness which in turn leads to misleading interpretation of the analysis. To handle the issue of uncertainty within accident data, the present work proposes a rough set theory (RST)-based approach to provide rule-based solution to the industry to minimize the number of accidents at work. Using RST and RST-based rule generation algorithm Learning by Example Module: Version 2 (LEM2), 279 important rules are extracted from the accident data obtained from an integrated steel industry to analyze the incident outcomes (injury, near miss and property damage). The results of the proposed methodology explore some of the important findings which are useful for the industry perspective. Therefore, the RST-based approach can be effective and efficient as well because of its potential to produce good results in the presence of uncertainty in data.