Department of Industrial Management, Center for Safety Management and Engineering, Tampere University of Technology, P.O. Box 541, FI-33101 Tampere, Finland.
Appl Ergon. 2013 Mar;44(2):215-24. doi: 10.1016/j.apergo.2012.07.001. Epub 2012 Aug 9.
The utilisation of data mining methods has become common in many fields. In occupational accident analysis, however, these methods are still rarely exploited. This study applies methods of data mining (decision tree and association rules) to the Finnish national occupational accidents and diseases statistics database to analyse factors related to slipping, stumbling, and falling (SSF) accidents at work from 2006 to 2007. SSF accidents at work constitute a large proportion (22%) of all accidents at work in Finland. In addition, they are more likely to result in longer periods of incapacity for work than other workplace accidents. The most important factor influencing whether or not an accident at work is related to SSF is the specific physical activity of movement. In addition, the risk of SSF accidents at work seems to depend on the occupation and the age of the worker. The results were in line with previous research. Hence the application of data mining methods was considered successful. The results did not reveal anything unexpected though. Nevertheless, because of the capability to illustrate a large dataset and relationships between variables easily, data mining methods were seen as a useful supplementary method in analysing occupational accident data.
数据挖掘方法在许多领域已经得到了广泛应用。然而，在职业事故分析中，这些方法仍然很少被利用。本研究应用数据挖掘方法（决策树和关联规则）对芬兰国家职业事故和疾病统计数据库进行分析，以研究 2006 年至 2007 年与工作中滑倒、绊倒和跌倒（SSF）事故相关的因素。在芬兰，工作中的 SSF 事故占所有工作事故的比例很大（22%）。此外，它们比其他工作场所事故更容易导致更长时间的无法工作。影响工作中是否发生 SSF 事故的最重要因素是具体的运动活动。此外，SSF 事故的风险似乎取决于职业和工人的年龄。研究结果与先前的研究一致。因此，数据挖掘方法的应用被认为是成功的。尽管结果没有揭示出任何意外的情况，但由于数据挖掘方法能够轻松地说明大型数据集和变量之间的关系，因此被视为分析职业事故数据的有用补充方法。
