Natural language processing-based ensemble technique to predict potential accident severity

In an effort to mitigate occupational hazards and promote proactive safety measures in industries, this study explores the application of ensemble learning and natural language processing (NLP) techniques to analyze the potential accident severity of hazards in a workplace. Even though the use of machine learning models based on reactive data is well-established in the domain of safety, the development of models using proactive data combining text reports and categorical features for predicting potential accident severity is comparatively new. Based on the road safety data collected through a Fatality Risk Control Programme (FRCP) initiative in an integrated steel plant in India, this study focuses on classifying accidents into different classes of severity. Dealing with unstructured texts and class-imbalanced data poses a significant challenge. In order to address the imbalance of classes of the target variable in the dataset, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. Insights from text data were extracted through NLP techniques, which were then used to develop a dataset with diverse features by incorporating categorical features. An ensemble model is developed by employing six prediction algorithms: Decision Tree, Random Forest, Naive Bayes, Support Vector Machine, Extreme Gradient Boosting or XGBoost, and Adaptive Boosting or AdaBoost. A soft voting ensemble was developed utilizing bagging learning and probabilistic aggregation approaches to yield an improved robust classification. Finally, the comparative importance of features is assessed through the Leave-One-Covariate-Out (LOCO) methodology. By integrating these techniques, the study presents a novel approach to anticipate accident severity beforehand, allowing authorities to take proactive interventions for improved workplace safety.