A decision support system developed with artificial neural networks for risk analysis and assessment in the occupational health and safety

People do not take some precautions in their work field before being exposed to some damages but taking precautions after these damages cause irreversible effects will not make any sense. In addition, everyone may not know about the precautions must be taken against danger so the presence of experts in work fields is required. For this reason, when experts foresee the risks and sign some precautions before an accident, the work fields will be more comfortable and safe areas. With the occupational health and safety law number 6331 published in the official gazette 30 June 2012, all employees were included in the occupational health and safety scope, which, therefore, gained more importance but the law farm out to people like having employers to employ occupational health and safety experts, occupational physicians and other health staff in work places. These professionals provide safe area in work places by preparing necessary substructure, taking precautions and keeping necessary records. However, because of the dangerous situations such as inattentiveness, tiredness, forgetfulness, inexperience, lack of information, not noticing interesting relations, machines and tools with no safety and with no periodic maintenance, and use of broken and defective machines, experts may also fail. To remove this deficiency, it is a necessity to sensibly use technology which is essential today. Computers will be taught a new system which supports decision making to minimize the damages of possible risks related to dangers and which helps take necessary precautions by using artificial nerve networks. The system responds to the newly met situations by learning those occurring after the risk analysis and evaluations. The technique aims to take more quality decisions by constantly incorporating the newly taken decisions in its system. Keywords: Occupational Health and Safety, Data Mining, Artificial Intelligence, Artificial Nerve Networks, Risk Analysis and Evaluation