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Dr Mashael Suliaman Maashi (BSc, MSc, PhD) دكتورة مشاعل بنت سليمان معشي

Associate Professor

Faculty, Director of the Research Center

علوم الحاسب والمعلومات
Building# 6, floor# 3, Office No#69
publication
Journal Article
2020

Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods

A, Mohammed M. . 2020

Nowadays, coronavirus (COVID-19) is getting international attention due it considered as
a life-threatened epidemic disease that hard to control the spread of infection around the world. Machine
learning (ML) is one of intelligent technique that able to automatically predict the event with reasonable
accuracy based on the experience and learning process. In the meantime, a rapid number of ML models have
been proposed for predicate the cases of COVID-19. Thus, there is need for an evaluation and benchmarking
of COVID-19 ML models which considered the main challenge of this study. Furthermore, there is no single
study have addressed the problem of evaluation and benchmarking of COVID diagnosis models. However,
this study proposed an intelligent methodology is to help the health organisations in the selection COVID-19
diagnosis system. The benchmarking and evaluation of diagnostic models for COVID-19 is not a trivial
process. There are multiple criteria requires to evaluate and some of the criteria are conicting with each
other. Our study is formulated as a decision matrix (DM) that embedded mix of ten evaluation criteria and
twelve diagnostic models for COVID-19. The multi-criteria decision-making (MCDM) method is employed
to evaluate and benchmarking the different diagnostic models for COVID19 with respect to the evaluation
criteria. An integrated MCDM method are proposed where TOPSIS applied for the benchmarking and
ranking purpose while Entropy used to calculate the weights of criteria. The study results revealed that the
benchmarking and selection problems associated with COVID19 diagnosis models can be effectively solved
using the integration of Entropy and TOPSIS. The SVM (linear) classier is selected as the best diagnosis
model for COVID19 with the closeness coefcient value of 0.9899 for our case study data. Furthermore,
the proposed methodology has solved the signicant variance for each criterion in terms of ideal best and
worst best value, beside issue when specic diagnosis models have same ideal best value.

Volume Number
8
Magazine \ Newspaper
IEEE Access
Pages
99115-99131
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