Occupational injury risk mitigation: Machine learning approach and feature optimization for smart workplace surveillance

Khairuddin, Mohamed Zul Fadhli and Hui, Puat Lu and Hasikin, Khairunnisa and Abd Razak, Nasrul Anuar and Lai, Khin Wee and Saudi, Ahmad Shakir Mohd and Ibrahim, Siti Salwa (2022) Occupational injury risk mitigation: Machine learning approach and feature optimization for smart workplace surveillance. International Journal of Environmental Research and Public Health, 19 (21). ISSN 1660-4601, DOI https://doi.org/10.3390/ijerph192113962.

Full text not available from this repository.


Forecasting the severity of occupational injuries shall be all industries' top priority. The use of machine learning is theoretically valuable to assist the predictive analysis, thus, this study attempts to propose a feature-optimized predictive model for anticipating occupational injury severity. A public database of 66,405 occupational injury records from OSHA is analyzed using five sets of machine learning models: Support Vector Machine, K-Nearest Neighbors, Naive Bayes, Decision Tree, and Random Forest. For model comparison, Random Forest outperformed other models with higher accuracy and F1-score. Therefore, it highlighted the potential of ensemble learning as a more accurate prediction model in the field of occupational injury. In constructing the model, this study also proposed the feature optimization technique that revealed the three most important features; `nature of injury', `type of event', and `affected body part' in developing model. The accuracy of the Random Forest model was improved by 0.5% or 0.895 and 0.954 for the prediction of hospitalization and amputation, respectively by redeveloping and optimizing the model with hyperparameter tuning. The feature optimization is essential in providing insight knowledge to the Safety and Health Practitioners for future injury corrective and preventive strategies. This study has shown promising potential for smart workplace surveillance.

Item Type: Article
Funders: None
Uncontrolled Keywords: Artificial intelligence; Machine learning; Occupational injury; Occupational safety and health; Features optimization
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Divisions: Faculty of Engineering > Biomedical Engineering Department
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 10 Nov 2023 08:40
Last Modified: 10 Nov 2023 08:40
URI: http://eprints.um.edu.my/id/eprint/40743

Actions (login required)

View Item View Item