Despite technological advancements and organizational adjustments, lost time accidents are major issues in occupational safety. However, there is very limited work that focuses on variables influencing days lost as a result of occupational accidents. In this study, decision tree and artificial neural network methods were used as machine learning techniques to investigate the impact of factors on accident lost day duration. Degree of injury, worker age, and worker activity were found to be the top three variables impacting loss of time from work. It was also identified that the mining method, location, and nature of injury had a moderate influence on duration lost due to occupational accidents. However, worker experience and ore type did not have any significant impact on the duration, which is an unexpected result. These results confirmed that some accident factors that might have a large influence on the number of mine accidents can be less critical when it comes to accident lost day duration.

尽管技术进步和组织调整，但误工事故仍是职业安全中的主要问题。但是，只有很少的工作集中在影响因职业事故导致的工作日损失的变量上。在这项研究中，决策树和人工神经网络方法被用作机器学习技术，以研究因素对事故损失工期的影响。发现伤害程度，工人年龄和工人活动是影响下班时间损失的前三个变量。还确定，采矿方法，位置和伤害性质对因职业事故造成的损失持续时间有中等影响。但是，工人的经验和矿石类型对工期没有任何重大影响，这是出乎意料的结果。