AI-enabled healthcare waste sorting using a multi-layer perceptron algorithm and its validation with Kolmogorov-Arnold networks

Ananya Kapoor, Vaibhav Sharma and Rajeev Agrawal

Chapter 19 in Handbook on Artificial Intelligence and the Circular Economy, 2026, pp 298-319 from Edward Elgar Publishing

Abstract: Healthcare waste requires careful handling and safe disposal based on the standard segregation categories. The disposal of healthcare waste also needs to be done on a priority basis and should be done correctly. An effective and detailed procedure should be followed to dispose of healthcare waste to prevent the spread of harmful and infectious diseases to waste handlers and the general public. Therefore, this study addresses the issue of sorting healthcare waste into different disposal categories at the disposal site so that their end step can be followed with the correct measures. The traditional Multi-Layer Perceptron (MLP) algorithm can sort the waste into different disposal categories. However, the MLPs use the fixed activation function on nodes and have linear weights. The chapter gives a promising alternative to these traditional MLPs, the Kolmogorov-Arnold Networks (KANs), which utilize learnable activation functions on edges and have non-linear weights. This proved to be better than the MLP in terms of accuracy as KAN has an accuracy of 93.17% and is stable while making the prediction.

Keywords: Healthcare; Waste sorting; Disposal techniques; Multi-layer perceptron; Kolmogorov-Arnold networks (search for similar items in EconPapers)
Date: 2026
ISBN: 9781035343379
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