Abstract
Decision support system (DSS) is a powerful tool which helps decision-makers take unbiased and insightful decisions from the historical data. In the domain of occupational accident analysis, decision-making should be effective, insightful, unbiased, and more importantly prompt. In order to obtain such decision, development of DSS is necessary. In the present study, an attempt has been made to build such DSS for accident analysis in an integrated steel plant. Two classifiers, i.e., support vector machine (SVM) and random forest (RF) have been used. RF produces better level of accuracy, i.e., \(99.34\%\). The developed DSS has full potential in making insightful decisions and can be used in other domains like manufacturing, construction, etc.
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Sarkar, S., Chain, M., Nayak, S., Maiti, J. (2019). Decision Support System for Prediction of Occupational Accident: A Case Study from a Steel Plant. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 813. Springer, Singapore. https://doi.org/10.1007/978-981-13-1498-8_69
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DOI: https://doi.org/10.1007/978-981-13-1498-8_69
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