The global mining industry has recorded significant declines in accident and injury rates attributed to the advancement in technology, increased enforcement, and safety consciousness. A goal of the mining industry is to achieve zero injury and occupational illness on all mine sites, prompting increased research into ways to further reduce mine accidents. A machine learning technique known as multiclass logistic regression is applied on a 10-year injury dataset from the Mine Safety and Health Administration (MSHA) to determine a miner’s susceptibility to a class of injury and to help identify significant risk factors associated with different classes of injury. The data is aggregated based on injury classification to provide statistically relevant results. The analysis identifies specific risk factors that influence a mine worker’s susceptibility to a given class of injury, i.e., non-fatal with no days lost or restricted activity, non-fatal with days lost and/or days of restricted work activity, and fatal and total permanent or partial permanent disability. These factors include miner’s age, mine type (coal vs. non-coal), experience on the current job (years), shift start time, employment type (operator vs. contractor), mining district, and type of accident. The results of the analysis indicate that a miner’s experience on the job, i.e., the number of years worked in a current job, is a significant risk to injury occurrence, even for those with decades of total mining experience. We further show the differences and similarities between the surface and underground mine incidents.

中文翻译：

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使用逻辑回归从 MSHA 事故和伤害数据中识别风险因素

由于技术进步、执法力度加大和安全意识提高，全球采矿业的事故和伤害率显着下降。采矿业的一个目标是在所有矿场实现零伤害和职业病，这促使加大研究力度以进一步减少矿山事故。一种称为多类逻辑回归的机器学习技术应用于来自矿山安全与健康管理局 (MSHA) 的 10 年伤害数据集，以确定矿工对一类伤害的易感性，并帮助识别与不同类别伤害相关的重要风险因素受伤。数据根据伤害分类进行汇总，以提供统计相关的结果。分析确定了影响矿工对给定类别伤害的易感性的特定风险因素，即非致命（无损失天数或活动受限）、非致命（损失天数和/或工作活动受限天数）以及致命和完全永久性或部分永久性残疾。这些因素包括矿工的年龄、矿山类型（煤与非煤）、当前工作的经验（年）、轮班开始时间、就业类型（操作员与承包商）、矿区和事故类型。分析结果表明，矿工的工作经验，即在当前工作中工作的年数，是发生伤害的重大风险，即使对于那些拥有数十年总采矿经验的人也是如此。我们进一步展示了地表和地下矿井事故之间的异同。