Environment Risk Assessment of Healthcare Waste Using Ensemble Learning Technique-Based EFMEA

Healthcare waste (HCW) affects the sustainable development of the environment greatly. The choice of medical waste treatment methods has become a significant concern for public health and safety due to the rapid surge in the volume and diversity of medical waste. As an important aspect of environmental risk assessment (ERA), HCW risk assessment plays a crucial role in environmental protection to develop sustainability strategies. Failure Mode and Effects Analysis (FMEA) has been extensively utilized in HCW risk assessment in the past few decades. In this paper, we aimed to address the limitations of traditional FMEA while incorporating the benefits of diverse FMEA methods and employ a novel ensemble learning technique-based FMEA method to perform risk assessment of HCW. A real-world case of HCW risk assessment is investigated for the verification of the performance and effectiveness of the ensemble learning technique-based Environment FMEA (EFMEA). Result of the case study shows that this ensemble learning technique-based EFMEA can provide a more reliable assessment result for HCW risk management.