## 6 Discussion and Future Research  
This study primarily aims to employ artificial intelligence technology to objectively and accurately classify occupational accident cases, rather than simply identifying or precisely understanding the causes of accidents. The focus lies on leveraging the collected examples of occupational accidents for more systematic classification. Traditional accident analysis methods have relied on case-by-case investigations of the occurrence process to determine specific causes. However, such methods face limitations when dealing with large-scale, accumulated cases, as they may narrow the scope of analysis or focus solely on particular causes or outcomes. On the other hand, artificial intelligence technology is well suited for handling large-scale recorded accident data and is highly effective for variable studies. Consequently, this study developed a model to classify accident types based on accumulated accident case data. The classification method achieved over 93% accuracy in Rank 1 results and secured over 97% accuracy in subsequent Rank 2 results. Enhancing the accuracy of AI models can be accomplished through improvements in model architecture, augmentation, or efficient preprocessing procedures. Nevertheless, these enhancements may not directly contribute to reducing occupational accidents or accurately identifying their root causes. Accidents in industrial sites may be caused by complex mechanisms or specific factors, which is a crucial consideration for determining the potential application of this study's findings. Therefore, to successfully implement artificial intelligence technology in the field of occupational safety and health, it is necessary to consider not only the technical completeness--such as model improvement and accuracy enhancement--but also how these technologies can contribute to the prevention of occupational accidents. In future research, to overcome these limitations and contribute more effectively to industrial accident prevention, the research will be expanded in the following directions:  
First, we will try to accurately classify the causal factors that directly contribute to the accident and identify the cause. In most accident cases, the original cause material is closely related to the occurrence of the accident. This is a factor that is commonly used or found in industrial sites such as machinery, equipment, parts, products, etc., and has the characteristic of coming into contact with or being close to workers when an accident occurs. Since this directly contributes to the cause of the accident, the exact cause and result of the accident can be predicted from the accident case. Therefore, referring to the method and results of this study, a model that can classify the original cause material from the accident overview can be added. This requires more information input than the results of the existing occurrence type, and the artificial intelligence model will also need to be improved. Second, a comprehensive accident analysis will be conducted, considering the complexity of the working environment. Modern industrial sites involve various risk factors that interact with each other due to technological advancements and system complexity. The complexity of the working environment encompasses not only direct causes such as causal factors but also systemic factors, organizational structures, human-machine interactions, and technical elements. [PERSON] et al. (2019) emphasized that failures in complex systems cannot be explained by simple causes, and [PERSON] et al. (2020) proposed a new risk management approach in the context of Industry 4.0. By referencing these studies, we will develop a model that comprehensively analyzes the diverse risk factors and their interactions that occur in the work environment. This will help identify systemic risk factors that are difficult to detect through causal factor analysis alone and contribute to establishing a comprehensive strategy for accident prevention.