What is it about?
This study focuses on reducing workplace accidents among plumbers in the construction industry. By using a strategy called the PDCA cycle (Plan, Do, Check, Act) and data mining techniques, we analyzed data from 200 workers to identify the main causes of accidents. We tested three algorithms to predict these accidents, finding that the Decision Tree method was particularly effective. With these insights, we can develop targeted measures to improve safety and reduce the risk of accidents in this field.
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Why is it important?
This research is important because it addresses the high incidence of occupational accidents among plumbers in the construction industry, which not only affects the health and safety of the workers but also impacts the productivity and financial health of the businesses involved. By developing a predictive model that can accurately identify risk factors and potential accidents, this study provides a tool for companies to proactively implement safety measures, ultimately reducing the number of injuries and improving overall workplace safety.
Perspectives
"By predicting accidents before they happen, this approach shifts the focus from reactive to proactive, potentially saving lives and resources and utilizing data mining and machine learning in occupational safety introduces a high-tech method to tackle age-old problems of workplace accidents, illustrating the potential of technology to improve worker safety. Effective prediction and management of occupational risks can guide policy makers in developing better regulations and standards to protect workers. these perspectives underscores the significance of the study in contributing to safer work environments in the construction industry and beyond".
Rodolfo Mosquera Navarro
Universidad Industrial de Santander
Read the Original
This page is a summary of: A model based on PDCA and data mining approach for the prevention of occupational accidents in the plumbing activity in the construction sector, Work, January 2024, IOS Press,
DOI: 10.3233/wor-230112.
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