Decision Analysis of the COVID-19 Vaccines

Ozsahin, Dilber Uzun; Gelisen, Mehmet Ilker; Taiwo, Mustapha; Agachan, Yasemin; Rahi, Dorrin; Uzun, Berna

doi:10.2478/ebtj-2021-0017

Cited by 19 publications

(7 citation statements)

References 20 publications

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“…They entail technical or economic considerations and the human factor, creating conflicts of interest and obstructing the final choice [ 31 ]. As a result, numerous instances of research utilizing MCDM have been conducted to optimize entire health systems [ 34 , 35 , 36 , 37 , 38 , 39 , 40 ].…”

Section: Methodsmentioning

confidence: 99%

Breast Cancer Screening Based on Supervised Learning and Multi-Criteria Decision-Making

Mustapha

Ozsahin

et al. 2022

Diagnostics

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On average, breast cancer kills one woman per minute. However, there are more reasons for optimism than ever before. When diagnosed early, patients with breast cancer have a better chance of survival. This study aims to employ a novel approach that combines artificial intelligence and a multi-criteria decision-making method for a more robust evaluation of machine learning models. The proposed machine learning techniques comprise various supervised learning algorithms, while the multi-criteria decision-making technique implemented includes the Preference Ranking Organization Method for Enrichment Evaluations. The Support Vector Machine, having achieved a net outranking flow of 0.1022, is ranked as the most favorable model for the early detection of breast cancer. The net outranking flow is the balance between the positive and negative outranking flows. This indicates that the higher the net flow, the better the alternative. K-nearest neighbor, logistic regression, and random forest classifier ranked second, third, and fourth, with net flows of 0.0316, −0.0032, and −0.0541, respectively. The least preferred alternative is the naive Bayes classifier with a net flow of −0.0766. The results obtained in this study indicate the use of the proposed method in making a desirable decision when selecting the most appropriate machine learning model. This gives the decision-maker the option of introducing new criteria into the decision-making process.

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Section: Methodsmentioning

confidence: 99%

Breast Cancer Screening Based on Supervised Learning and Multi-Criteria Decision-Making

Mustapha

Ozsahin

et al. 2022

Diagnostics

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“…Clinical signs are not observed in either clinical trials or current immunization undertakings. Post-vaccination side effects are often minor [ 77 ]. However, vaccination research should be conducted to address low vaccine confidence and uptake.…”

Section: Challengesmentioning

confidence: 99%

“…WHO and other governing bodies have formulated guidelines and remedial activities to minimize the spread of COVID-19. As the death ratio caused by COVID-19 increases, the most suitable vaccine for people should be selected [ 76 ], and the best approaches for distributing such vaccines and other treatment forms must also be established [ 77 ]. Mass vaccination campaigns (MVCs) are implemented to control and remove infectious diseases in large populations [ 45 ].…”

Section: Motivationsmentioning

confidence: 99%

“…Another recommendation is to classify COVID-19 vaccine priority groups on the basis of availability, safety, delivery and cost [ 46 ]. Furthermore, it is recommended to analyze more COVID-19 vaccines and provide additional criteria to the MCDM approaches on the basis of individual priorities [ 77 ]. Ranking hospitals during the COVID-19 pandemic is recommended to identify more criteria, including the quality of equipment and hospital staff satisfaction, along with the quality of services and patients satisfaction [ 55 ].…”

Section: Recommendationsmentioning

confidence: 99%

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Systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions

Alamoodi

Zaidan

Albahri

et al. 2023

Complex Intell. Syst.

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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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“…Since the outbreak began, there have been more than 100 million confirmed cases of COVID-19 and more than 2 million deaths worldwide. The pandemic has had a significant impact on global health, economies, and daily life, with many countries implementing measures, such as lockdowns, travel restrictions, and mask mandates, in an effort to slow the spread of the virus [ 1 , 2 ]. The COVID-19 pandemic has affected countries all around the world, with some regions being more severely impacted than others.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach

Ozsahin

Onakpojeruo²,

Duwa³

et al. 2023

Diagnostics

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The novel coronavirus (COVID-19), also known as SARS-CoV-2, is a highly contagious respiratory disease that first emerged in Wuhan, China in 2019 and has since become a global pandemic. The virus is spread through respiratory droplets produced when an infected person coughs or sneezes, and it can lead to a range of symptoms, from mild to severe. Some people may not have any symptoms at all and can still spread the virus to others. The best way to prevent the spread of COVID-19 is to practice good hygiene. It is also important to follow the guidelines set by local health authorities, such as physical distancing and quarantine measures. The World Health Organization (WHO), on the other hand, has classified this virus as a pandemic, and as a result, all nations are attempting to exert control and secure all public spaces. The current study aimed to (I) compare the weekly COVID-19 cases between Israel and Greece, (II) compare the monthly COVID-19 mortality cases between Israel and Greece, (III) evaluate and report the influence of the vaccination rate on COVID-19 mortality cases in Israel, and (IV) predict the number of COVID-19 cases in Israel. The advantage of completing these tasks is the minimization of the spread of the virus by deploying different mitigations. To attain our objective, a correlation analysis was carried out, and two distinct artificial intelligence (AI)-based models—specifically, an artificial neural network (ANN) and a classical multiple linear regression (MLR)—were developed for the prediction of COVID-19 cases in Greece and Israel by utilizing related variables as the input variables for the models. For the evaluation of the models, four evaluation metrics (determination coefficient (R2), mean square error (MSE), root mean square error (RMSE), and correlation coefficient (R)) were considered in order to determine the performance of the deployed models. From a variety of perspectives, the corresponding determination coefficient (R2) demonstrated the statistical advantages of MLR over the ANN model by following a linear pattern. The MLR predictive model was both efficient and accurate, with 98% accuracy, while ANN showed 94% accuracy in the effective prediction of COVID-19 cases.

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Decision Analysis of the COVID-19 Vaccines

Cited by 19 publications

References 20 publications

Breast Cancer Screening Based on Supervised Learning and Multi-Criteria Decision-Making

Breast Cancer Screening Based on Supervised Learning and Multi-Criteria Decision-Making

Systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions

COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach

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