Predicting Di-2-Ethylhexyl Phthalate Toxicity: Hybrid Integrated Harris Hawks Optimization With Support Vector Machines

Phthalic acid esters (PAEs) are organic pollutants and synthetic compounds and have adverse effects on human health. In this study, we investigated whether Di-2-Ethylhexyl phthalate (DEHP), one of many PAEs, has adverse effects on rats. Adult male Sprague-Dawley rats were treated daily by oral gavag...

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Autores principales:	Beibei Shi, Ali Asghar Heidari, Cheng Chen, Mingjing Wang, Changcheng Huang, Huiling Chen, Jiayin Zhu
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2020
Materias:	Di-2-Ethylhexyl phthalate hepatotoxicity support vector machine Harris hawks optimization salp swarm algorithm grey wolf optimizer Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/84e29ac98b1942b7a9822aefd62e33ac
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spelling	oai:doaj.org-article:84e29ac98b1942b7a9822aefd62e33ac2021-11-20T00:00:34ZPredicting Di-2-Ethylhexyl Phthalate Toxicity: Hybrid Integrated Harris Hawks Optimization With Support Vector Machines2169-353610.1109/ACCESS.2020.3020895https://doaj.org/article/84e29ac98b1942b7a9822aefd62e33ac2020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9183935/https://doaj.org/toc/2169-3536Phthalic acid esters (PAEs) are organic pollutants and synthetic compounds and have adverse effects on human health. In this study, we investigated whether Di-2-Ethylhexyl phthalate (DEHP), one of many PAEs, has adverse effects on rats. Adult male Sprague-Dawley rats were treated daily by oral gavage with vehicle (corn oil) or DEHP at a dose of 3000 mg/kg/day for 15 days. The results showed that DEHP caused hepatotoxicity in rats. When compared with the control group, relative liver weights, and serum alanine, aminotransferase levels significantly increased after DEHP exposure. Hepatocyte swelling and degeneration were also found in DEHP-exposed rats. This study proposes an effective intelligence framework for the prediction of DEHP poisoning. The framework is designed by integrating an enhanced Harris hawks optimization (HHO) with a support vector machine (SVM), which is called SGLHHO-SVM. The core characteristic of the developed methodology is the SGLHHO algorithm that integrates the levy mechanism and two core operators abstracted from the salp swarm algorithm and grey wolf optimizer to enhance and restore the search capabilities of the HHO. The presented SGLHHO approach is used to tackle the key parameter pair optimization of the SVM, and it is also utilized to grab the optimal feature subset. Regarding the optimal feature subset and the pair parameter simultaneously, SGLHHO-SVM can autonomously predict the DEHP poisoning. The developed SGLHHO was conducted on 23 benchmark problems and compared with other state-of-the-art and competitive methods. The results demonstrate that the designed SGLHHO performs superior to other competitors on most benchmark problems. Furthermore, the proposed SGLHHO-SVM is also compared with other machine learning algorithms on a real-life DEHP sampled data. Statistical results verify the proposal can show better predictive property and higher stability on all for metrics. Therefore, the SGLHHO-SVM may be served as a potential computer-aided tool for the prediction of DEHP poisoning.Beibei ShiAli Asghar HeidariCheng ChenMingjing WangChangcheng HuangHuiling ChenJiayin ZhuIEEEarticleDi-2-Ethylhexyl phthalatehepatotoxicitysupport vector machineHarris hawks optimizationsalp swarm algorithmgrey wolf optimizerElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 8, Pp 161188-161202 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Di-2-Ethylhexyl phthalate hepatotoxicity support vector machine Harris hawks optimization salp swarm algorithm grey wolf optimizer Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Di-2-Ethylhexyl phthalate hepatotoxicity support vector machine Harris hawks optimization salp swarm algorithm grey wolf optimizer Electrical engineering. Electronics. Nuclear engineering TK1-9971 Beibei Shi Ali Asghar Heidari Cheng Chen Mingjing Wang Changcheng Huang Huiling Chen Jiayin Zhu Predicting Di-2-Ethylhexyl Phthalate Toxicity: Hybrid Integrated Harris Hawks Optimization With Support Vector Machines
description	Phthalic acid esters (PAEs) are organic pollutants and synthetic compounds and have adverse effects on human health. In this study, we investigated whether Di-2-Ethylhexyl phthalate (DEHP), one of many PAEs, has adverse effects on rats. Adult male Sprague-Dawley rats were treated daily by oral gavage with vehicle (corn oil) or DEHP at a dose of 3000 mg/kg/day for 15 days. The results showed that DEHP caused hepatotoxicity in rats. When compared with the control group, relative liver weights, and serum alanine, aminotransferase levels significantly increased after DEHP exposure. Hepatocyte swelling and degeneration were also found in DEHP-exposed rats. This study proposes an effective intelligence framework for the prediction of DEHP poisoning. The framework is designed by integrating an enhanced Harris hawks optimization (HHO) with a support vector machine (SVM), which is called SGLHHO-SVM. The core characteristic of the developed methodology is the SGLHHO algorithm that integrates the levy mechanism and two core operators abstracted from the salp swarm algorithm and grey wolf optimizer to enhance and restore the search capabilities of the HHO. The presented SGLHHO approach is used to tackle the key parameter pair optimization of the SVM, and it is also utilized to grab the optimal feature subset. Regarding the optimal feature subset and the pair parameter simultaneously, SGLHHO-SVM can autonomously predict the DEHP poisoning. The developed SGLHHO was conducted on 23 benchmark problems and compared with other state-of-the-art and competitive methods. The results demonstrate that the designed SGLHHO performs superior to other competitors on most benchmark problems. Furthermore, the proposed SGLHHO-SVM is also compared with other machine learning algorithms on a real-life DEHP sampled data. Statistical results verify the proposal can show better predictive property and higher stability on all for metrics. Therefore, the SGLHHO-SVM may be served as a potential computer-aided tool for the prediction of DEHP poisoning.
format	article
author	Beibei Shi Ali Asghar Heidari Cheng Chen Mingjing Wang Changcheng Huang Huiling Chen Jiayin Zhu
author_facet	Beibei Shi Ali Asghar Heidari Cheng Chen Mingjing Wang Changcheng Huang Huiling Chen Jiayin Zhu
author_sort	Beibei Shi
title	Predicting Di-2-Ethylhexyl Phthalate Toxicity: Hybrid Integrated Harris Hawks Optimization With Support Vector Machines
title_short	Predicting Di-2-Ethylhexyl Phthalate Toxicity: Hybrid Integrated Harris Hawks Optimization With Support Vector Machines
title_full	Predicting Di-2-Ethylhexyl Phthalate Toxicity: Hybrid Integrated Harris Hawks Optimization With Support Vector Machines
title_fullStr	Predicting Di-2-Ethylhexyl Phthalate Toxicity: Hybrid Integrated Harris Hawks Optimization With Support Vector Machines
title_full_unstemmed	Predicting Di-2-Ethylhexyl Phthalate Toxicity: Hybrid Integrated Harris Hawks Optimization With Support Vector Machines
title_sort	predicting di-2-ethylhexyl phthalate toxicity: hybrid integrated harris hawks optimization with support vector machines
publisher	IEEE
publishDate	2020
url	https://doaj.org/article/84e29ac98b1942b7a9822aefd62e33ac
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Predicting Di-2-Ethylhexyl Phthalate Toxicity: Hybrid Integrated Harris Hawks Optimization With Support Vector Machines

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