Application of Data Mining to Predict Work Accident Rates using Rapid Miner
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Abstract
Working safety is strongly linked to machines, aircraft, and work tools in the workplace runway and its surroundings, as well as how to work. The corporation must provide this protection since it is a human right. According to ILO (International Labor Organization) estimates, 2 million people die each year as a result of work-related issues around the world. A total of 354,000 persons died as a result of these accidents. The rate of fatal accidents in underdeveloped countries is four times that in developed countries. The agriculture, construction, mining, forestry, and fisheries industries all have hazardous jobs that account for the majority of accidents. This research is descriptive in nature, examining work accidents that occur based on secondary data and making predictions to estimate the amount of work accidents using a Data Mining approach utilizing Rapid Miner to determine the level of work accidents. Rapid miner is a data mining processing software that includes tools for creating decision trees and a data mining engine that may be used in its own products. The data utilized was collected from the Industrial Safety and Health Analytics Database as secondary data. The database's content consists primarily of accident records from 12 distinct factories in three different nations, with each row representing a 439-data-row accident incident. According to the findings, 11 of the 12 factories have an accident rate of level I; the third factory (level 03) has an accident rate of level IV on the Risco Critico power lock; and factory 11 (local 11) does not have a crash lift.
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