Application of optimized machine learning techniques for prediction of occupational accidents

Although, the usefulness of the machine learning (ML) technique in predicting future outcomes has been established in different domains of applications (e.g., heath care), its exploration in predicting accidents in occupational safety domain is almost new. This necessitates the investigation of ML techniques in predicting accidents. But, ML-based algorithms cannot produce the best performance until its parameters are properly tuned or optimized. Moreover, only the selection of efficient optimized classifier may not fulfil the overall decision-making purposes as it cannot explain the inter-relationships among the factors behind the occurrence of accidents. Hence, in addition to prediction, decision-making rules are required to be extracted from the accident data. Considering the above-mentioned issues, in this research, optimized machine learning algorithms have been applied to predict the accident outcomes such as injury, near miss, and property damage using occupational accident data. Two popular machine learning algorithms, namely support vector machine (SVM) and artificial neural network (ANN) have been used whose parameters are optimized by two powerful optimization algorithms, namely genetic algorithm (GA) and particle swarm optimization (PSO) in order to achieve higher degree of accuracy and robustness. PSO-based SVM outperforms the other algorithms with the highest level of accuracy and robustness. Furthermore, rules are extracted by incorporating decision tree C5.0 algorithm with PSO-based SVM model. Finally, a set of nine useful rules are extracted to identify the root causes of the injury, near miss and property damage cases. A case study from a steel plant is presented in support of the proposed methodology.