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Su X, Sun Y, Liu H, Lang Q, Zhang Y, Zhang J, Wang C, Chen Y. An innovative ensemble model based on deep learning for predicting COVID-19 infection. Sci Rep 2023; 13:12322. [PMID: 37516796 PMCID: PMC10387055 DOI: 10.1038/s41598-023-39408-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 07/25/2023] [Indexed: 07/31/2023] Open
Abstract
Nowadays, global public health crises are occurring more frequently, and accurate prediction of these diseases can reduce the burden on the healthcare system. Taking COVID-19 as an example, accurate prediction of infection can assist experts in effectively allocating medical resources and diagnosing diseases. Currently, scholars worldwide use single model approaches or epidemiology models more often to predict the outbreak trend of COVID-19, resulting in poor prediction accuracy. Although a few studies have employed ensemble models, there is still room for improvement in their performance. In addition, there are only a few models that use the laboratory results of patients to predict COVID-19 infection. To address these issues, research efforts should focus on improving disease prediction performance and expanding the use of medical disease prediction models. In this paper, we propose an innovative deep learning model Whale Optimization Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM) and Artificial Neural Network (ANN) called WOCLSA which incorporates three models ANN, CNN and LSTM. The WOCLSA model utilizes the Whale Optimization Algorithm to optimize the neuron number, dropout and batch size parameters in the integrated model of ANN, CNN and LSTM, thereby finding the global optimal solution parameters. WOCLSA employs 18 patient indicators as predictors, and compares its results with three other ensemble deep learning models. All models were validated with train-test split approaches. We evaluate and compare our proposed model and other models using accuracy, F1 score, recall, AUC and precision metrics. Through many studies and tests, our results show that our prediction models can identify patients with COVID-19 infection at the AUC of 91%, 91%, and 93% respectively. Other prediction results achieve a respectable accuracy of 92.82%, 92.79%, and 91.66% respectively, f1-score of 93.41%, 92.79%, and 92.33% respectively, precision of 93.41%, 92.79%, and 92.33% respectively, recall of 93.41%, 92.79%, and 92.33% respectively. All of these exceed 91%, surpassing those of comparable models. The execution time of WOCLSA is also an advantage. Therefore, the WOCLSA ensemble model can be used to assist in verifying laboratory research results and predict and to judge various diseases in public health events.
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Affiliation(s)
- Xiaoying Su
- School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China
| | - Yanfeng Sun
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China
| | - Hongxi Liu
- School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China
| | - Qiuling Lang
- School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China
| | - Yichen Zhang
- School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China
| | - Jiquan Zhang
- School of Environment, Northeast Normal University, Changchun, 130024, China
| | - Chaoyong Wang
- School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China.
| | - Yanan Chen
- School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China
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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.
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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
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Duarte I, Ribeiro MC, Pereira MJ, Leite PP, Peralta-Santos A, Azevedo L. Spatiotemporal evolution of COVID-19 in Portugal's Mainland with self-organizing maps. Int J Health Geogr 2023; 22:4. [PMID: 36710328 PMCID: PMC9884330 DOI: 10.1186/s12942-022-00322-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 12/23/2022] [Indexed: 01/30/2023] Open
Abstract
BACKGROUND Self-Organizing Maps (SOM) are an unsupervised learning clustering and dimensionality reduction algorithm capable of mapping an initial complex high-dimensional data set into a low-dimensional domain, such as a two-dimensional grid of neurons. In the reduced space, the original complex patterns and their interactions can be better visualized, interpreted and understood. METHODS We use SOM to simultaneously couple the spatial and temporal domains of the COVID-19 evolution in the 278 municipalities of mainland Portugal during the first year of the pandemic. Temporal 14-days cumulative incidence time series along with socio-economic and demographic indicators per municipality were analyzed with SOM to identify regions of the country with similar behavior and infer the possible common origins of the incidence evolution. RESULTS The results show how neighbor municipalities tend to share a similar behavior of the disease, revealing the strong spatiotemporal relationship of the COVID-19 spreading beyond the administrative borders of each municipality. Additionally, we demonstrate how local socio-economic and demographic characteristics evolved as determinants of COVID-19 transmission, during the 1st wave school density per municipality was more relevant, where during 2nd wave jobs in the secondary sector and the deprivation score were more relevant. CONCLUSIONS The results show that SOM can be an effective tool to analysing the spatiotemporal behavior of COVID-19 and synthetize the history of the disease in mainland Portugal during the period in analysis. While SOM have been applied to diverse scientific fields, the application of SOM to study the spatiotemporal evolution of COVID-19 is still limited. This work illustrates how SOM can be used to describe the spatiotemporal behavior of epidemic events. While the example shown herein uses 14-days cumulative incidence curves, the same analysis can be performed using other relevant data such as mortality data, vaccination rates or even infection rates of other disease of infectious nature.
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Affiliation(s)
- Igor Duarte
- grid.9983.b0000 0001 2181 4263Formely: Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal
| | - Manuel C. Ribeiro
- grid.9983.b0000 0001 2181 4263CERENA/DER, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal
| | - Maria João Pereira
- grid.9983.b0000 0001 2181 4263CERENA/DER, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal
| | - Pedro Pinto Leite
- grid.420634.70000 0001 0807 4731Direção de Serviços de Informação e Análise, Direção-Geral da Saúde, Lisbon, Portugal
| | - André Peralta-Santos
- grid.420634.70000 0001 0807 4731Direção de Serviços de Informação e Análise, Direção-Geral da Saúde, Lisbon, Portugal ,grid.10772.330000000121511713NOVA National School of Public Health, Public Health Research Centre, Universidade NOVA de Lisboa, Lisbon, Portugal ,grid.10772.330000000121511713Comprehensive Health Research Centre (CHRC), Universidade NOVA de Lisboa, Lisbon, Portugal
| | - Leonardo Azevedo
- CERENA/DER, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001, Lisbon, Portugal.
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