Leveraging Machine Learning for Optimized Biomedical Waste Prediction in India: A Comprehensive Overview

Abstract

Biomedical waste (BMW) is a significant concern in India's rapidly expanding healthcare sector, where improper regulation and disposal systems prevail. Machine Learning (ML) offers substantial benefits to enhance BMW management through effective waste prediction, classification, and treatment. This paper discusses the prospects and issues in applying ML in the given case. Although the deep learning models achieve high accuracy, they have a black-box nature, which limits their interpretability for many healthcare professionals. Furthermore, the lack of standardized high-quality training datasets fails to give exact predictive analytics across vast systems of healthcare programming, making the model generalization and scalability challenge complex. Real-time integration remains an alarming challenge, as many current studies are based on offline predictions. Issues of data privacy, security, model complexity, and computational resource requirements add to the challenges and difficulties inherent in adoption, especially in resource-constrained facilities. To address these limitations, this paper recommends developing customized ML models for identifying various types of waste to enhance the accuracy of classification and disposal efficiency. It also highlights the need for better data quality, design of interpretable models, real-time integration, and implementation of cost-effective and resource-efficient solutions to reach the potential of ML for the management of BMW. Conclusively, further research in these fields will guarantee a more secure and sustainable BMW management ecosystem.