AI-Enabled Predictive Analytics towards Sustainable Hospital Waste Management: A Machine Learning Framework in Line with SDGs

DOI

10.2991/978-94-6463-948-3_39How to use a DOI?

Keywords

Artificial Intelligence; Machine Learning; Hospital Waste Management; Sustainable Development Goals; Predictive Analytics

Abstract

Objective

The goal of this study is to develop and verify an AI-based predictive analytics system for hospital waste management in response to the challenges of conventional biomedical waste management practices, which are reactive and inefficient in dealing with waste spikes [11]. Novelty: This research is novel in combining healthcare administration, sustainability science, and machine learning (ML)–based decision support in compliance with the United Nations Sustainable Development Goals (SDGs) [8]. With the use of hospital datasets, Central Pollution Control Board (CPCB), World Health Organization (WHO), and case studies, the research employed Random Forest Regression, Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) networks. Model performance was measured using RMSE, MAE, R², and accuracy. Results: The LSTM model had the highest accuracy (92%), followed by Random Forest and SVM. The model improved segregation efficiency by 35%, lowered landfill contributions by 28%, and lowered incineration-related carbon emissions by 18% compared to state-of-the-art method [4]. Implications: The results indicate the capability of AI-based waste forecasting to revolutionize hospital sustainability operations. Implementation of AI dashboards, IoT-enabled containers, and blockchain traceability can save money, improve compliance, and facilitate SDG 3 (Health), SDG 12 (Responsible Consumption), and SDG 13 (Climate Action) [5].

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Niravkumar R. Joshi
AU  - Darshana Upadhayay
PY  - 2026
DA  - 2026/01/06
TI  - AI-Enabled Predictive Analytics towards Sustainable Hospital Waste Management: A Machine Learning Framework in Line with SDGs
BT  - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
PB  - Atlantis Press
SP  - 558
EP  - 566
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-948-3_39
DO  - 10.2991/978-94-6463-948-3_39
ID  - Joshi2026
ER  -