An optimization-based decision tree approach for predicting slip-trip-fall accidents at work

Slip-trip-fall (STF) accident is one of the leading causes of injuries. Therefore, prediction of STF is necesscary prior to its occurrence at workplaces. Although there exist a number of studies analysing STFs, machine learning (ML)-based approaches for both predicting STF and analysing its factors remain an unexplored area of research. Therefore, the aim of the study is to develop a novel methodology for prediction of STF occurrences using decision tree (DT) classifiers, namely C5.0, classification and regression tree (CART) and random forest (RF). The parameters of the classifiers are optimized using two state-of-the-art optimization algorithms, namely particle swarm optimization (PSO), and genetic algorithm (GA) for enhanced prediction accuracy. Experimental results reveal that PSO-RP algorithm produces the best accuracy as compared to others. Finally, the proposed method generates a set of 20 interpretable safety decision rules explaining the factors behind the occurrences of STFs.