Neuro-fuzzy prediction model of occupational injuries in mining.
Int J Occup Saf Ergon
; : 1-10, 2024 Sep 23.
Article
in En
| MEDLINE
| ID: mdl-39314012
ABSTRACT
Objectives. This study investigates the possibility of developing a unique model for predicting work-related injuries in Serbian underground coal mines using neural networks and fuzzy logic theory. Accidents are common due to the unique nature of underground mineral extraction involving people, machinery and limited workplaces. Methods. A universal model for predicting occupational accidents takes into account influential factors such as organizational aspects, personal and collective protective equipment, on-the-job training and leadership factors. The selected networks achieved a prediction accuracy of >90%. Results. The study successfully identifies potential risks and critical worker groups leading to injuries. The sensitivity analysis provides insights for targeted safety measures and improved organizational practices. Conclusion. This data-driven approach makes a valuable contribution to safety in the mining industry. Implementation of the predictive model can reduce injuries and machine damage, and improve worker well-being.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Int J Occup Saf Ergon
Journal subject:
MEDICINA OCUPACIONAL
/
PSICOLOGIA
Year:
2024
Document type:
Article
Country of publication:
United kingdom