1
|
Ozsahin DU, Isa NA, Uzun B, Ozsahin I. Quantifying holistic capacity response and healthcare resilience in tackling COVID-19: Assessment of country capacity by MCDM. PLoS One 2024; 19:e0294625. [PMID: 38578767 PMCID: PMC10997098 DOI: 10.1371/journal.pone.0294625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 10/31/2023] [Indexed: 04/07/2024] Open
Abstract
The resilience of a country during the COVID-19 pandemic was determined based in whether it was holistically prepared and responsive. This resilience can only be identified through systematic data collection and analysis. Historical evidence-based response indicators have been proven to mitigate pandemics like COVID-19. However, most databases are outdated, requiring updating, derivation, and explicit interpretation to gain insight into the impact of COVID-19. Outdated databases do not show a country's true preparedness and response capacity, therefore, it undermines pandemic threat. This study uses up-to-date evidence-based pandemic indictors to run a cross-country comparative analysis of COVID-19 preparedness, response capacity, and healthcare resilience. PROMETHEE-a multicriteria decision making (MCDM) technique-is used to quantify the strengths (positive) and weaknesses (negative) of each country's COVID-19 responses, with full ranking (net) from best to least responsive. From 22 countries, South Korea obtained the highest net outranking value of 0.1945, indicating that it was the most resilient, while Mexico had the lowest (-0.1428). Although countries were underprepared, there was a robust response to the pandemic, especially in developing countries. This study demonstrates the performance and response capacity of 22 key countries to resist COVID-19, from which other countries can compare their statutory capacity ranking in order to learn/adopt the evidence-based responses of better performing countries to improve their resilience.
Collapse
Affiliation(s)
- Dilber Uzun Ozsahin
- Medical Diagnostic Imaging Department, College of Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- Operational Research Center in Healthcare, Near East University, Nicosia/TRNC, Mersin, Turkey
- Department of Biomedical Engineering, Faculty of Engineering, Near East University, Nicosia/TRNC, Mersin, Turkey
| | - Nuhu Abdulhaqq Isa
- Department of Biomedical Engineering, Faculty of Engineering, Near East University, Nicosia/TRNC, Mersin, Turkey
- Department of Biomedical Technology, Nasarawa State College of Health Science and Technology, Keffi, Nasarawa State, Nigeria
| | - Berna Uzun
- Operational Research Center in Healthcare, Near East University, Nicosia/TRNC, Mersin, Turkey
- Department of Statistics, Carlos III University of Madrid, Madrid, Spain
| | - Ilker Ozsahin
- Operational Research Center in Healthcare, Near East University, Nicosia/TRNC, Mersin, Turkey
- Department of Biomedical Engineering, Faculty of Engineering, Near East University, Nicosia/TRNC, Mersin, Turkey
- Brain Health Imaging Institute, Department of Radiology, Weill Cornell Medicine, New York, New York, United States of America
| |
Collapse
|
2
|
Asuquo DE, Attai KF, Johnson EA, Obot OU, Adeoye OS, Akwaowo CD, Ekpenyong N, Isiguzo C, Ekanem U, Motilewa O, Dan E, Umoh E, Ekpin V, Uzoka FME. Multi-criteria decision analysis method for differential diagnosis of tropical febrile diseases. Health Informatics J 2024; 30:14604582241260659. [PMID: 38860564 DOI: 10.1177/14604582241260659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2024]
Abstract
This paper employs the Analytical Hierarchy Process (AHP) to enhance the accuracy of differential diagnosis for febrile diseases, particularly prevalent in tropical regions where misdiagnosis may have severe consequences. The migration of health workers from developing countries has resulted in frontline health workers (FHWs) using inadequate protocols for the diagnosis of complex health conditions. The study introduces an innovative AHP-based Medical Decision Support System (MDSS) incorporating disease risk factors derived from physicians' experiential knowledge to address this challenge. The system's aggregate diagnostic factor index determines the likelihood of febrile illnesses. Compared to existing literature, AHP models with risk factors demonstrate superior prediction accuracy, closely aligning with physicians' suspected diagnoses. The model's accuracy ranges from 85.4% to 96.9% for various diseases, surpassing physicians' predictions for Lassa, Dengue, and Yellow Fevers. The MDSS is recommended for use by FHWs in communities lacking medical experts, facilitating timely and precise diagnoses, efficient application of diagnostic test kits, and reducing overhead expenses for administrators.
Collapse
Affiliation(s)
- Daniel E Asuquo
- Department of Information Systems, Faculty of Computing, University of Uyo, Uyo, Nigeria
| | - Kingsley F Attai
- Department of Mathematics & Computer Science, Ritman University, Ikot Ekpene, Nigeria
| | - Ekemini A Johnson
- Department of Mathematics & Computer Science, Ritman University, Ikot Ekpene, Nigeria
| | - Okure U Obot
- Department of Software Engineering, Faculty of Computing, University of Uyo, Uyo, Nigeria
| | - Olufemi S Adeoye
- Department of Data Science, Faculty of Computing, University of Uyo, Uyo, Nigeria
| | - Christie Divine Akwaowo
- Community Medicine Department, University of Uyo, Uyo, Nigeria
- Health Systems Research Hub, University of Uyo Teaching Hospital, Uyo, Nigeria
| | - Nnette Ekpenyong
- Community Health Department, University of Calabar, Calabar, Nigeria
| | | | - Uwemedimbuk Ekanem
- Community Medicine Department, University of Uyo, Uyo, Nigeria
- Institute of Health Research and Development, University of Uyo Teaching Hospital, Uyo, Nigeria
| | - Olugbemi Motilewa
- Community Medicine Department, University of Uyo, Uyo, Nigeria
- Health Systems Research Hub, University of Uyo Teaching Hospital, Uyo, Nigeria
- Institute of Health Research and Development, University of Uyo Teaching Hospital, Uyo, Nigeria
| | - Emem Dan
- Health Systems Research Hub, University of Uyo Teaching Hospital, Uyo, Nigeria
| | - Edidiong Umoh
- Health Systems Research Hub, University of Uyo Teaching Hospital, Uyo, Nigeria
| | - Victory Ekpin
- Health Systems Research Hub, University of Uyo Teaching Hospital, Uyo, Nigeria
| | | |
Collapse
|
3
|
Complex q-rung orthopair fuzzy 2-tuple linguistic group decision-making framework with Muirhead mean operators. Artif Intell Rev 2023. [DOI: 10.1007/s10462-023-10408-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
|
4
|
Alamoodi AH, Zaidan BB, Albahri OS, Garfan S, Ahmaro IYY, Mohammed RT, Zaidan AA, Ismail AR, Albahri AS, Momani F, Al-Samarraay MS, Jasim AN, R.Q.Malik. Systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions. COMPLEX INTELL SYST 2023; 9:1-27. [PMID: 36777815 PMCID: PMC9895977 DOI: 10.1007/s40747-023-00972-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 01/01/2023] [Indexed: 02/05/2023]
Abstract
When COVID-19 spread in China in December 2019, thousands of studies have focused on this pandemic. Each presents a unique perspective that reflects the pandemic's main scientific disciplines. For example, social scientists are concerned with reducing the psychological impact on the human mental state especially during lockdown periods. Computer scientists focus on establishing fast and accurate computerized tools to assist in diagnosing, preventing, and recovering from the disease. Medical scientists and doctors, or the frontliners, are the main heroes who received, treated, and worked with the millions of cases at the expense of their own health. Some of them have continued to work even at the expense of their lives. All these studies enforce the multidisciplinary work where scientists from different academic disciplines (social, environmental, technological, etc.) join forces to produce research for beneficial outcomes during the crisis. One of the many branches is computer science along with its various technologies, including artificial intelligence, Internet of Things, big data, decision support systems (DSS), and many more. Among the most notable DSS utilization is those related to multicriterion decision making (MCDM), which is applied in various applications and across many contexts, including business, social, technological and medical. Owing to its importance in developing proper decision regimens and prevention strategies with precise judgment, it is deemed a noteworthy topic of extensive exploration, especially in the context of COVID-19-related medical applications. The present study is a comprehensive review of COVID-19-related medical case studies with MCDM using a systematic review protocol. PRISMA methodology is utilized to obtain a final set of (n = 35) articles from four major scientific databases (ScienceDirect, IEEE Xplore, Scopus, and Web of Science). The final set of articles is categorized into taxonomy comprising five groups: (1) diagnosis (n = 6), (2) safety (n = 11), (3) hospital (n = 8), (4) treatment (n = 4), and (5) review (n = 3). A bibliographic analysis is also presented on the basis of annual scientific production, country scientific production, co-occurrence, and co-authorship. A comprehensive discussion is also presented to discuss the main challenges, motivations, and recommendations in using MCDM research in COVID-19-related medial case studies. Lastly, we identify critical research gaps with their corresponding solutions and detailed methodologies to serve as a guide for future directions. In conclusion, MCDM can be utilized in the medical field effectively to optimize the resources and make the best choices particularly during pandemics and natural disasters.
Collapse
Affiliation(s)
- A. H. Alamoodi
- Faculty of Computing and Meta-Technology (FKMT), Universiti Pendidikan Sultan Idris (UPSI), Perak, Malaysia
| | - B. B. Zaidan
- Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliu, Yunlin 64002 Taiwan, ROC
| | - O. S. Albahri
- Computer Techniques Engineering Department, Mazaya University College, Nasiriyah, Iraq
| | - Salem Garfan
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim, Malaysia
| | - Ibraheem Y. Y. Ahmaro
- Computer Science Department, College of Information Technology, Hebron University, Hebron, Palestine
| | - R. T. Mohammed
- Department of Computing Science, Komar University of Science and Technology (KUST), Sulaymaniyah, Iraq
| | - A. A. Zaidan
- SP Jain School of Global Management, Sydney, Australia
| | - Amelia Ritahani Ismail
- Department of Computer Science, Kulliyyah of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur, Malaysia
| | - A. S. Albahri
- Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq
| | - Fayiz Momani
- E-Business and Commerce Department, Faculty of Administrative and Financial Sciences, University of Petra, Amman, 961343 Jordan
| | - Mohammed S. Al-Samarraay
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim, Malaysia
| | | | - R.Q.Malik
- Medical Intrumentation Techniques Engineering Department, Al-Mustaqbal University College, Babylon, Iraq
| |
Collapse
|
5
|
Hospital selection framework for remote MCD patients based on fuzzy q-rung orthopair environment. Neural Comput Appl 2023; 35:6185-6196. [PMID: 36415285 PMCID: PMC9672551 DOI: 10.1007/s00521-022-07998-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 10/25/2022] [Indexed: 11/18/2022]
Abstract
This research proposes a novel mobile health-based hospital selection framework for remote patients with multi-chronic diseases based on wearable body medical sensors that use the Internet of Things. The proposed framework uses two powerful multi-criteria decision-making (MCDM) methods, namely fuzzy-weighted zero-inconsistency and fuzzy decision by opinion score method for criteria weighting and hospital ranking. The development of both methods is based on a Q-rung orthopair fuzzy environment to address the uncertainty issues associated with the case study in this research. The other MCDM issues of multiple criteria, various levels of significance and data variation are also addressed. The proposed framework comprises two main phases, namely identification and development. The first phase discusses the telemedicine architecture selected, patient dataset used and decision matrix integrated. The development phase discusses criteria weighting by q-ROFWZIC and hospital ranking by q-ROFDOSM and their sub-associated processes. Weighting results by q-ROFWZIC indicate that the time of arrival criterion is the most significant across all experimental scenarios with (0.1837, 0.183, 0.230, 0.276, 0.335) for (q = 1, 3, 5, 7, 10), respectively. Ranking results indicate that Hospital (H-4) is the best-ranked hospital in all experimental scenarios. Both methods were evaluated based on systematic ranking and sensitivity analysis, thereby confirming the validity of the proposed framework.
Collapse
|
6
|
Albahri AS, Al-qaysi ZT, Alzubaidi L, Alnoor A, Albahri OS, Alamoodi AH, Bakar AA. A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology. Int J Telemed Appl 2023; 2023:7741735. [PMID: 37168809 PMCID: PMC10164869 DOI: 10.1155/2023/7741735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 02/01/2023] [Accepted: 03/16/2023] [Indexed: 05/13/2023] Open
Abstract
The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and IEEE, were considered to gather relevant scientific and theoretical articles. Initially, 125 papers were found between 2010 and 2021 related to this integrated research field. After the filtering process, only 30 articles were identified and classified into five categories based on their type of deep learning methods. The first category, convolutional neural network (CNN), accounts for 70% (n = 21/30). The second category, recurrent neural network (RNN), accounts for 10% (n = 3/30). The third and fourth categories, deep neural network (DNN) and long short-term memory (LSTM), account for 6% (n = 30). The fifth category, restricted Boltzmann machine (RBM), accounts for 3% (n = 1/30). The literature's findings in terms of the main aspects identified in existing applications of deep learning pattern recognition techniques in SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, and achieved classification accuracies, are examined. A comprehensive mapping analysis was also conducted, which identified six categories. Current challenges of ensuring trustworthy deep learning in SSVEP-based BCI applications were discussed, and recommendations were provided to researchers and developers. The study critically reviews the current unsolved issues of SSVEP-based BCI applications in terms of development challenges based on deep learning techniques and selection challenges based on multicriteria decision-making (MCDM). A trust proposal solution is presented with three methodology phases for evaluating and benchmarking SSVEP-based BCI applications using fuzzy decision-making techniques. Valuable insights and recommendations for researchers and developers in the SSVEP-based BCI and deep learning are provided.
Collapse
Affiliation(s)
- A. S. Albahri
- Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq
| | - Z. T. Al-qaysi
- Department of Computer Science, Computer Science and Mathematics College, Tikrit University, Tikrit, Iraq
| | - Laith Alzubaidi
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia
- ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | | | - O. S. Albahri
- Computer Techniques Engineering Department, Mazaya University College, Nasiriyah, Iraq
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, Australia
| | - A. H. Alamoodi
- Faculty of Computing and Meta-Technology (FKMT), Universiti Pendidikan Sultan Idris (UPSI), Perak, Malaysia
| | | |
Collapse
|
7
|
Sharma V, Zivic F, Adamovic D, Ljusic P, Kotorcevic N, Slavkovic V, Grujovic N. Multi-Criteria Decision Making Methods for Selection of Lightweight Material for Railway Vehicles. MATERIALS (BASEL, SWITZERLAND) 2022; 16:ma16010368. [PMID: 36614707 PMCID: PMC9822170 DOI: 10.3390/ma16010368] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 12/14/2022] [Accepted: 12/16/2022] [Indexed: 06/01/2023]
Abstract
This paper deals with the selection of the optimal material for railway wagons, from among three different steel and three aluminium based materials, by using four different Multicriteria Decision Making Methods (MCDM) and comparing their ranking of the materials. We analysed: Dual-Phase 600 steel, Transformation-Induced Plasticity (TRIP) 700 steel, Twinning-Induced Plasticity (TWIP) steel, Aluminium (Al) alloys, Al 6005-T6, and Al 6082-T6, and porous Al structure with closed cells. Four different MCDM methods were used: VIKOR, TOPSIS, PROMETTHEE and the Weighted aggregated sum product assessment method (WASPAS). Key material properties that were used in the MCDM analysis were: density, yield strength (Y.S.), tensile strength (T.S.), Y.S./T.S. ratio, Youngs modulus (Y.M.), cost and corrosion resistance (C.R.). Research results indicate that aluminium and its alloys prove to be the most suitable material, based on setup criteria. Advanced steels also achieved good ranking, making them a valid option, immediately behind lightweight aluminium alloys. Porous aluminium did not perform well, according to the used MDCM methods, mainly due to the significantly lower strength exhibited by the porous structures in general.
Collapse
Affiliation(s)
- Varun Sharma
- Faculty of Engineering, University of Kragujevac, 34000 Kragujevac, Serbia
| | - Fatima Zivic
- Faculty of Engineering, University of Kragujevac, 34000 Kragujevac, Serbia
| | - Dragan Adamovic
- Faculty of Engineering, University of Kragujevac, 34000 Kragujevac, Serbia
| | - Petar Ljusic
- Faculty of Engineering, University of Kragujevac, 34000 Kragujevac, Serbia
- AMM Manufacturing, 34325 Kragujevac, Serbia
| | - Nikola Kotorcevic
- Faculty of Engineering, University of Kragujevac, 34000 Kragujevac, Serbia
- AMM Manufacturing, 34325 Kragujevac, Serbia
| | - Vukasin Slavkovic
- Faculty of Engineering, University of Kragujevac, 34000 Kragujevac, Serbia
| | - Nenad Grujovic
- Faculty of Engineering, University of Kragujevac, 34000 Kragujevac, Serbia
| |
Collapse
|
8
|
Sharaf IM. The differential measure for Pythagorean fuzzy multiple criteria group decision-making. COMPLEX INTELL SYST 2022; 9:3333-3354. [PMID: 36530758 PMCID: PMC9734832 DOI: 10.1007/s40747-022-00913-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 11/06/2022] [Indexed: 12/12/2022]
Abstract
Pythagorean fuzzy sets (PFSs) proved to be powerful for handling uncertainty and vagueness in multi-criteria group decision-making (MCGDM). To make a compromise decision, comparing PFSs is essential. Several approaches were introduced for comparison, e.g., distance measures and similarity measures. Nevertheless, extant measures have several defects that can produce counter-intuitive results, since they treat any increase or decrease in the membership degree the same as the non-membership degree; although each parameter has a different implication. This study introduces the differential measure (DFM) as a new approach for comparing PFSs. The main purpose of the DFM is to eliminate the unfair arguments resulting from the equal treatment of the contradicting parameters of a PFS. It is a preference relation between two PFSs by virtue of position in the attribute space and according to the closeness of their membership and non-membership degrees. Two PFSs are classified as identical, equivalent, superior, or inferior to one another giving the degree of superiority or inferiority. The basic properties of the proposed DFM are given. A novel method for multiple criteria group decision-making is proposed based on the introduced DFM. A new technique for computing the weights of the experts is developed. The proposed method is applied to solve two applications, the evaluation of solid-state drives and the selection of the best photovoltaic cell. The results are compared with the results of some extant methods to illustrate the applicability and validity of the method. A sensitivity analysis is conducted to examine its stability and practicality.
Collapse
Affiliation(s)
- Iman Mohamad Sharaf
- Department of Basic Sciences, Higher Technological Institute, Tenth of Ramadan City, Egypt
| |
Collapse
|
9
|
Albahri AS, Zaidan AA, AlSattar HA, A. Hamid R, Albahri OS, Qahtan S, Alamoodi AH. Towards physician's experience: Development of machine learning model for the diagnosis of autism spectrum disorders based on complex
T
‐spherical fuzzy‐weighted zero‐inconsistency method. Comput Intell 2022. [DOI: 10.1111/coin.12562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Ahmed S. Albahri
- Informatics Institute for Postgraduate Studies (IIPS) Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq
| | - Aws A. Zaidan
- Faculty of Engineering and IT The British University in Dubai Dubai United Arab Emirates
| | - Hassan A. AlSattar
- Department of Business Administration, College of Administrative Sciences The University of Mashreq Baghdad Iraq
| | - Rula A. Hamid
- Informatics Institute for Postgraduate Studies (IIPS) Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq
| | - Osamah S. Albahri
- Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq
| | - Sarah Qahtan
- Department of Computer Center, College of Health and Medical Techniques Middle Technical University Baghdad Iraq
| | - Abdulla H. Alamoodi
- Department of Computing, Faculty of Arts, Computing and Creative Industry Universiti Pendidikan Sultan Idris Tanjung Malim Malaysia
| |
Collapse
|
10
|
Hybrid Diagnosis Models for Autism Patients Based on Medical and Sociodemographic Features Using Machine Learning and Multicriteria Decision-Making (MCDM) Techniques: An Evaluation and Benchmarking Framework. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:9410222. [DOI: 10.1155/2022/9410222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 10/01/2022] [Accepted: 10/18/2022] [Indexed: 11/18/2022]
Abstract
Background and Contexts. Autism spectrum disorder (ASD) is difficult to diagnose, prompting researchers to increase their efforts to find the best diagnosis by introducing machine learning (ML). Recently, several available challenges and issues have been highlighted for the diagnosis of ASD. High consideration must be taken into the feature selection (FS) approaches and classification process simultaneously by using medical tests and sociodemographic characteristic features in autism diagnostic. The constructed ML models neglected the importance of medical tests and sociodemographic features in a training and evaluation dataset, especially since some features have different contributions to the processing data and possess more relevancies to the classification information than others. However, the role of the physician’s experience towards feature contributions remains limited. In addition, the presence of many evaluation criteria, criteria trade-offs, and criteria importance categorize the evaluation and benchmarking of diagnosis ML models concerning the intersection between FS approaches and ML classification methods given under complex multicriteria decision-making (MCDM) problems. To date, no study has presented an evaluation framework for benchmarking the best hybrid diagnosis models to classify autism patients’ emergency levels considering multicriteria evaluation solutions. Method. The three-phase framework integrated the MCDM and ML to develop the diagnosis models and evaluate and benchmark the best. Firstly, the new ASD-dataset-combined medical tests and sociodemographic characteristic features is identified and preprocessed. Secondly, developing the hybrid diagnosis models using the intersection process between three FS techniques and five ML algorithms introduces 15 models. The selected medical tests and sociodemographic features from each FS technique are weighted before feeding the five ML algorithms using the fuzzy-weighted zero-inconsistency (FWZIC) method based on four psychiatry experts. Thirdly, (i) formulate a dynamic decision matrix for all developed models based on seven evaluation metrics, including classification accuracy, precision, F1 score, recall, test time, train time, and AUC. (ii) The fuzzy decision by opinion score method (FDOSM) is used to evaluate and benchmark the 15 models concerning the seven evaluation metrics. Results. Results reveal that (i) the three FS techniques have obtained a size different from the others in the number of the selected features; the sets were 39, 38, and 41 out of 48 features. Each set has its weights constructed by FWIZC. Considered sociodemographic features have been mostly selected more than medical tests within FS techniques. (ii) The first three best hybrid models were “ReF-decision tree,” “IG-decision tree,” and “Chi2-decision tree,” with score values 0.15714, 0.17539, and 0.29444. The best diagnosis model (ReF-decision tree) has obtained 0.4190, 0.0030, 0.9946, 0.9902, 0.9902, 0.9902, 0.9902, and 0.9951 for the C1=train time, C2=test time, C3=AUC, C4=CA, C5=F1 score, C6=precision, and C7=recall, respectively. The developed framework would be beneficial in advancing, accelerating, and selecting diagnosis tools in therapy with ASD. The selected model can identify severity as light, medium, or intense based on medical tests and sociodemographic weighted features.
Collapse
|
11
|
Xu D, Xu Z. Bibliometric analysis of decision-making in healthcare management from 1998 to 2021. INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 2022. [DOI: 10.1080/20479700.2022.2134641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Duo Xu
- Business School, Stevens Institute of Technology, Hoboken, NJ, USA
| | - Zeshui Xu
- Business School, Sichuan University, Chengdu, People’s Republic of China
| |
Collapse
|
12
|
Diagnosis-Based Hybridization of Multimedical Tests and Sociodemographic Characteristics of Autism Spectrum Disorder Using Artificial Intelligence and Machine Learning Techniques: A Systematic Review. Int J Telemed Appl 2022; 2022:3551528. [PMID: 35814280 PMCID: PMC9270139 DOI: 10.1155/2022/3551528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 05/31/2022] [Accepted: 06/13/2022] [Indexed: 11/18/2022] Open
Abstract
Autism spectrum disorder (ASD) is a complex neurobehavioral condition that begins in childhood and continues throughout life, affecting communication and verbal and behavioral skills. It is challenging to discover autism in the early stages of life, which prompted researchers to intensify efforts to reach the best solutions to treat this challenge by introducing artificial intelligence (AI) techniques and machine learning (ML) algorithms, which played an essential role in greatly assisting the medical and healthcare staff and trying to obtain the highest predictive results for autism spectrum disorder. This study is aimed at systematically reviewing the literature related to the criteria, including multimedical tests and sociodemographic characteristics in AI techniques and ML contributions. Accordingly, this study checked the Web of Science (WoS), Science Direct (SD), IEEE Xplore digital library, and Scopus databases. A set of 944 articles from 2017 to 2021 is collected to reveal a clear picture and better understand all the academic literature through a definitive collection of 40 articles based on our inclusion and exclusion criteria. The selected articles were divided based on similarity, objective, and aim evidence across studies. They are divided into two main categories: the first category is “diagnosis of ASD based on questionnaires and sociodemographic features” (
). This category contains a subsection that consists of three categories: (a) early diagnosis of ASD towards analysis, (b) diagnosis of ASD towards prediction, and (c) diagnosis of ASD based on resampling techniques. The second category consists of “diagnosis ASD based on medical and family characteristic features” (
). This multidisciplinary systematic review revealed the taxonomy, motivations, recommendations, and challenges of diagnosis ASD research in utilizing AI techniques and ML algorithms that need synergistic attention. Thus, this systematic review performs a comprehensive science mapping analysis and identifies the open issues that help accomplish the recommended solution of diagnosis ASD research. Finally, this study critically reviews the literature and attempts to address the diagnosis ASD research gaps in knowledge and highlights the available ASD datasets, AI techniques and ML algorithms, and the feature selection methods that have been collected from the final set of articles.
Collapse
|
13
|
Radulescu CZ, Radulescu M, Boncea R. A Multi-Criteria Decision Support and Application to the Evaluation of the Fourth Wave of COVID-19 Pandemic. ENTROPY 2022; 24:e24050642. [PMID: 35626527 PMCID: PMC9141305 DOI: 10.3390/e24050642] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/28/2022] [Accepted: 04/30/2022] [Indexed: 12/10/2022]
Abstract
The COVID-19 pandemic caused important health and societal damage across the world in 2020–2022. Its study represents a tremendous challenge for the scientific community. The correct evaluation and analysis of the situation can lead to the elaboration of the most efficient strategies and policies to control and mitigate its propagation. The paper proposes a Multi-Criteria Decision Support (MCDS) based on the combination of three methods: the Group Analytic Hierarchy Process (GAHP), which is a subjective group weighting method; Extended Entropy Weighting Method (EEWM), which is an objective weighting method; and the COmplex PRoportional ASsessment (COPRAS), which is a multi-criteria method. The COPRAS uses the combined weights calculated by the GAHP and EEWM. The sum normalization (SN) is considered for COPRAS and EEWM. An extended entropy is proposed in EEWM. The MCDS is implemented for the development of a complex COVID-19 indicator called COVIND, which includes several countries’ COVID-19 indicators, over a fourth COVID-19 wave, for a group of European countries. Based on these indicators, a ranking of the countries is obtained. An analysis of the obtained rankings is realized by the variation of two parameters: a parameter that describes the combination of weights obtained with EEWM and GAHP and the parameter of extended entropy function. A correlation analysis between the new indicator and the general country indicators is performed. The MCDS provides policy makers with a decision support able to synthesize the available information on the fourth wave of the COVID-19 pandemic.
Collapse
Affiliation(s)
- Constanta Zoie Radulescu
- National Institute for Research and Development in Informatics, 8-10, Mareşal Averescu, 011455 Bucharest, Romania; (C.Z.R.); (R.B.)
| | - Marius Radulescu
- “Gheorghe Mihoc-Caius Iacob” Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy, Calea 13 Septembrie, No. 13, 050711 Bucharest, Romania
- Correspondence:
| | - Radu Boncea
- National Institute for Research and Development in Informatics, 8-10, Mareşal Averescu, 011455 Bucharest, Romania; (C.Z.R.); (R.B.)
| |
Collapse
|
14
|
Brzyska J, Szamrej-Baran I. Economic vulnerability assessment of EU countries to the impact of COVID-19 pandemic with the revised CEV index. PROCEDIA COMPUTER SCIENCE 2022; 207:3244-3253. [PMID: 36275376 PMCID: PMC9578939 DOI: 10.1016/j.procs.2022.09.382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The COVID-19 pandemic had a wide range of detrimental consequences for the global and national economies. It is vital to identify particularly susceptible areas to adopt effective strategies to alleviate the adverse effects of a pandemic. The objective of the paper is to assess the economic vulnerability of EU countries to the COVID-19 pandemic impact using the revised CEV Index. In the study, methods of multivariate statistics were used to analyse the effects of the pandemic. The revised CEVI replaces the 20-dimensional set of features with one aggregate measure, estimated for 27 EU Member States. According to the study, the economic vulnerability of EU countries to the COVID-19 pandemic varies significantly. The most vulnerable countries are in southern Europe, where the tourism sector plays a significant role in GDP composition. Highly susceptible are also Baltic countries: Latvia and Lithuania. The pandemic's harmful impact was the least seen in Germany and Scandinavian countries. The results of this study can be used as a tool for the formulation of policies aimed at overcoming the adverse consequences of economic vulnerability. The CEVI indicates certain areas in the country's economy that make it more fragile. Thus, it can play a significant role in the decision-making process. In the event of a pandemic shock, the CEVI, in combination with other tools, can be an effective instrument for improving the economy's resilience and helping it recover faster.
Collapse
Affiliation(s)
- Joanna Brzyska
- Institute of Economics and Finance, University of Szczecin, ul. Mickiewicza 64, 71-101 Szczecin, Poland
| | - Izabela Szamrej-Baran
- Institute of Economics and Finance, University of Szczecin, ul. Mickiewicza 64, 71-101 Szczecin, Poland
| |
Collapse
|