ICL Chair in Sustainable Mining, Polytechnic University of Catalonia, 08034 Barcelona, Spain.
Department of Mathematics, Polytechnic University of Catalonia, 08034 Barcelona, Spain.
Int J Environ Res Public Health. 2018 Mar 7;15(3):462. doi: 10.3390/ijerph15030462.
An analysis of occupational accidents in the mining sector was conducted using the data from the Spanish Ministry of Employment and Social Safety between 2005 and 2015, and data-mining techniques were applied. Data was processed with the software Weka. Two scenarios were chosen from the accidents database: surface and underground mining. The most important variables involved in occupational accidents and their association rules were determined. These rules are composed of several predictor variables that cause accidents, defining its characteristics and context. This study exposes the 20 most important association rules in the sector-either surface or underground mining-based on the statistical confidence levels of each rule as obtained by Weka. The outcomes display the most typical immediate causes, along with the percentage of accidents with a basis in each association rule. The most important immediate cause is body movement with physical effort or overexertion, and the type of accident is physical effort or overexertion. On the other hand, the second most important immediate cause and type of accident are different between the two scenarios. Data-mining techniques were chosen as a useful tool to find out the root cause of the accidents.
采用 2005 年至 2015 年西班牙就业和社会安全部的数据对采矿业职业事故进行了分析，并应用了数据挖掘技术。使用 Weka 软件对数据进行了处理。从事故数据库中选择了两个场景：露天开采和地下开采。确定了涉及职业事故的最重要变量及其关联规则。这些规则由导致事故的几个预测变量组成，定义了事故的特征和背景。本研究根据 Weka 获得的每个规则的统计置信水平，在该行业（无论是露天开采还是地下开采）中展示了 20 个最重要的关联规则。结果显示了最典型的直接原因，以及每个关联规则所依据的事故百分比。最重要的直接原因是身体运动伴有体力劳动或过度劳累，事故类型也是体力劳动或过度劳累。另一方面，两个场景的第二个最重要的直接原因和事故类型不同。选择数据挖掘技术作为找出事故根本原因的有用工具。
