Investigating Machine Learning Applications in the Prediction of Occupational Injuries in South African National Parks

Abstract

There is a need to predict occupational injuries in South African National Parks for the purpose of implementing targeted interventions or preventive measures. Machine-learning models have the capability of predicting injuries such that the employees that are at risk of experiencing occupational injuries can be identified. Support Vector Machines (SVMs), k Nearest Neighbours (k-NN), XGB classifier and Deep Neural Networks were applied and overall performance was compared to the accuracy of baseline models that always predict low extremity injuries. Data extracted from the Department of Employment and Labour’s Compensation Fund was used for training the models. SVMs had the best performance in predicting between low extremity injuries and injuries in the torso and hands regions. However, the overall accuracy was 56%, which was slightly above the baseline and below findings from similar previous research that reported a minimum of 62%. Gender was the only feature with an importance score significantly greater than zero. There is a need to use more features related to work conditions and which acknowledge the importance of environment in order to improve the accuracy of the predictions of the models. Furthermore, more types of injuries, and employees that have not experienced any injuries, should be included in future studies.

Keywords

• machine learning
• prediction
• occupational injuries
• national parks