Application of machine learning techniques in the prediction of excess lifetime cancer risks of agricultural byproducts used as building and construction

Oyebisi, S.O and Owamah, H.I. (2023) Application of machine learning techniques in the prediction of excess lifetime cancer risks of agricultural byproducts used as building and construction. Cleaner Waste Systems.

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Abstract

Recycling improves the circular economy and resource sustainability by using agricultural waste to create new products. However, agricultural byproducts resulting from the recycling of agricultural waste materials contain naturally occurring radionuclides with potential risks to human health and the environment. Therefore, this article provides an overview of relevant literature on radiological properties of agricultural byproducts (rice husk ash, mussel shell, palm oil clinker, and palm oil fuel ash), prioritizing their specific activities (226Ra, 232Th and 40K). Consequently, absorbed gamma dose rates (AGDR), annual effective dose rates (AEDR) and excess lifetime cancer risks (ELCR) of the agricultural byproducts studied were determined. The specific concentrations, AGDR, AEDR, and ECLR were trained, validated, and tested using various machine learning algorithms. An evaluation of the radiological properties of all agricultural byproducts examined revealed that they pose no risk of cancer. Additionally, compared to support vector machine, regression trees, ensemble trees, Gaussian process regression, and neural networks, linear regression yielded the best performance metrics, making it the most suitable technique for predicting excess lifetime cancer risks of the surveyed agricultural byproducts.

Item Type:	Article
Uncontrolled Keywords:	CancerMachine learning algorithmRadioactivityRecyclingSustainable productionWaste management
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Engineering, Science and Mathematics > School of Civil Engineering and the Environment
Depositing User:	AKINWUMI
Date Deposited:	27 Oct 2023 07:55
Last Modified:	27 Oct 2023 07:55
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/17451

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