[en] Recently, significant attention has been devoted to vaccine-derived poliovirus (VDPV) surveillance due to its severe consequences. Prediction of the outbreak incidence of VDPF requires an accurate analysis of the alarming data. The overarching aim to this study is to develop a novel hybrid machine learning approach to identify the key parameters that dominate the outbreak incidence of VDPV. The proposed method is based on the integration of random vector functional link (RVFL) networks with a robust optimization algorithm called whale optimization algorithm (WOA). WOA is applied to improve the accuracy of the RVFL network by finding the suitable parameter configurations for the algorithm. The classification performance of the WOA-RVFL method is successfully validated using a number of datasets from the UCI machine learning repository. Thereafter, the method is implemented to track the VDPV outbreak incidences recently occurred in several provinces in Lao People's Democratic Republic. The results demonstrate the accuracy and efficiency of the WOA-RVFL algorithm in detecting the VDPV outbreak incidences, as well as its superior performance to the traditional RVFL method.
Disciplines :
Biotechnologie
Auteur, co-auteur :
HEMEDAN, Ahmed ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core
Abd Elaziz, Mohamed ; Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt. abd_el_aziz_m@yahoo.com ; School of Computer Science& Technology, Huazhong university of Science and Technology, Wuhan, 430074, China. abd_el_aziz_m@yahoo.com
Jiao, Pengcheng; Ocean College, Zhejiang University, Zhoushan, 316021, Zhejiang, China
Alavi, Amir H; Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA, USA ; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA ; Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan
Bahgat, Mahmoud; Research Group Immune- and Bio-markers for Infection, the Center of Excellence for Advanced Sciences, the National Research Center, Cairo, Egypt ; Therapeutic Chemistry Department, the National Research Center, Cairo, Egypt
OSTASZEWSKI, Marek ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core
SCHNEIDER, Reinhard ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core
Ghazy, Haneen A; Biotechnology department, Animal Health research institute, Kafrelsheikh, Egypt
Ewees, Ahmed A; Department of Computer, Damietta University, Damietta El-Gadeeda City, Egypt
Lu, Songfeng; School of Computer Science& Technology, Huazhong university of Science and Technology, Wuhan, 430074, China
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
Prediction of the Vaccine-derived Poliovirus Outbreak Incidence: A Hybrid Machine Learning Approach.
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