Evaluation of multi-criteria decision making and risk analysis methods in occupational health and safety and a new proactive risk analysis approach

Occupational health and safety is a critical element in protecting employees' lives and ensuring the sustainability of businesses. Risk analysis methods are employed to safeguard workers and fully ensure production safety in occupational health and safety practices. Risk analyses aim to prevent accidents and adverse situations by identifying potential hazards in advance, making them one of the most effective methods. The risk analysis approach is a systematic framework developed to control specific factors in workplace occupational health and safety. The purpose of risk management and its implementation is to establish an effective safety network to prevent invisible hazards from materializing by organizing the most valid and verified information about incidents causing occupational accidents and diseases, as well as the factors triggering them. Using risk analysis methods is essential for eliminating high-risk situations or minimizing hazards in workplaces. In this context, this thesis study examines various risk analysis methods applied in occupational health and safety risk assessments, develops a new risk analysis method, and compares it using multi-criteria decision-making methods. Subsequently, correlations were established with artificial neural networks in machine learning. The newly developed Proactive Risk Analysis Method and other risk analysis methods were applied separately to a defined sample group, enabling comparison and evaluation. Through multi-criteria decision-making methods and machine learning, the validity and reliability of the risk analysis methods used in the thesis were assessed.