Author
Listed:
- A. S. Albahri
(Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia2Iraqi Commission for Computers & Informatics, Informatics Institute for Postgraduate Studies, Baghdad, Iraq)
- Jameel R. Al-Obaidi
(Department of Biology, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia)
- A. A. Zaidan
(Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia)
- O. S. Albahri
(Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia)
- Rula A. Hamid
(Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia2Iraqi Commission for Computers & Informatics, Informatics Institute for Postgraduate Studies, Baghdad, Iraq)
- B. B. Zaidan
(Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia)
- A. H. Alamoodi
(Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia)
- M. Hashim
(Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia)
Abstract
Coronavirus disease (COVID-19) pandemic has a tremendous effect on people’s lives worldwide, and the number of infected patients increases daily. The healthcare sector is affected by a large number of patients with COVID-19, and a solution is urgently needed to avert the risk of deteriorating patients in terms of prioritizing patients based on their health conditions. Prioritization of patients with COVID-19 is a complex and multi-criteria decision-analysis (MCDA) problem due to (i) multiple biological laboratory examination criteria, (ii) criteria importance and (iii) trade-off amongst the criteria. This study presents a new multi-biological laboratory examination framework for prioritizing patients with COVID-19 on the basis of integrated MCDA methods. The experiment was conducted on the basis of three phases. In the first phase, patient datasets containing eight biological laboratory examination criteria for six patients with COVID-19 were derived and discussed. The outcome of this phase was used to propose a decision matrix on the basis of the intersection between “biological laboratory examination criteria” and “COVID-19 patients list”. In the second phase, the analytic hierarchy process (AHP) method was utilized to set the subjective weights for the biological laboratory examination criteria by respiratory experts. In the last phase, the VIekriterijumsko KOmpromisno Rangiranje (VIKOR) method was adopted to prioritize patients in the context of individual and group decision making (GDM). Results showed that (1) the integration of AHP–VIKOR method based on individual and GDM contexts was effective for solving prioritization problems for patients with COVID-19, and (2) the prioritization results of patients with COVID-19 showed no variation in the internal and external VIKOR GDM contexts. The proposed multi-biological laboratory examination framework can differentiate between the mild and serious or critical condition of patients with COVID-19 by prioritizing them based on integrated AHP–VIKOR methods. In conclusion, medical sectors can use the proposed framework to differentiate the health conditions of infected patients and to assign appropriate care with prompt and effective treatment.
Suggested Citation
A. S. Albahri & Jameel R. Al-Obaidi & A. A. Zaidan & O. S. Albahri & Rula A. Hamid & B. B. Zaidan & A. H. Alamoodi & M. Hashim, 2020.
"Multi-Biological Laboratory Examination Framework for the Prioritization of Patients with COVID-19 Based on Integrated AHP and Group VIKOR Methods,"
International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(05), pages 1247-1269, August.
Handle:
RePEc:wsi:ijitdm:v:19:y:2020:i:05:n:s0219622020500285
DOI: 10.1142/S0219622020500285
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Citations
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Cited by:
- Cuoghi, Kaio Guilherme & Leoneti, Alexandre Bevilacqua & Passador, João Luiz, 2022.
"On the choice of public or private management models in the Brazilian Unified Health System (SUS),"
Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
- Albahri, A.S. & Alnoor, Alhamzah & Zaidan, A.A. & Albahri, O.S. & Hameed, Hamsa & Zaidan, B.B. & Peh, S.S. & Zain, A.B. & Siraj, S.B. & Alamoodi, A.H. & Yass, A.A., 2021.
"Based on the multi-assessment model: Towards a new context of combining the artificial neural network and structural equation modelling: A review,"
Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
- Tayarani N., Mohammad-H., 2021.
"Applications of artificial intelligence in battling against covid-19: A literature review,"
Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
- Noor S. Baqer & A. S. Albahri & Hussein A. Mohammed & A. A. Zaidan & Rula A. Amjed & Abbas M. Al-Bakry & O. S. Albahri & H. A. Alsattar & Alhamzah Alnoor & A. H. Alamoodi & B. B. Zaidan & R. Q. Malik , 2022.
"Indoor air quality pollutants predicting approach using unified labelling process-based multi-criteria decision making and machine learning techniques,"
Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 81(4), pages 591-613, December.
- Almulhim, Tarifa & Barahona, Igor, 2023.
"An extended picture fuzzy multicriteria group decision analysis with different weights: A case study of COVID-19 vaccine allocation,"
Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
- Hassad de Andrade, Liz & Moreira Antunes, Jorge Junio & Araújo de Medeiros, Antônio Mamede & Wanke, Peter & Nunes, Bernardo Pereira, 2022.
"The impact of social welfare and COVID-19 stringency on the perceived utility of food apps: A hybrid MCDM approach,"
Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
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