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

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 conflicting 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) classifier is selected as the best diagnosis model for COVID19 with the closeness coefficient value of 0.9899 for our case study data. Furthermore, the proposed methodology has solved the significant variance for each criterion in terms of ideal best and worst best value, beside issue when specific diagnosis models have same ideal best value.

[1]  N. Arunkumar,et al.  A real time computer aided object detection of nasopharyngeal carcinoma using genetic algorithm and artificial neural network based on Haar feature fear , 2018, Future Gener. Comput. Syst..

[2]  P. Piro,et al.  Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligence and Regression Analysis , 2020, Sustainability.

[3]  Y. Hu,et al.  Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China , 2020, The Lancet.

[4]  B. B. Zaidan,et al.  Evaluation and selection of open-source EMR software packages based on integrated AHP and TOPSIS , 2015, J. Biomed. Informatics.

[5]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[6]  Yunna Wu,et al.  What are the critical barriers to the development of hydrogen refueling stations in China? A modified fuzzy DEMATEL approach , 2020 .

[7]  N. Arunkumar,et al.  Examining multiple feature evaluation and classification methods for improving the diagnosis of Parkinson’s disease , 2019, Cognitive Systems Research.

[8]  K. Yuen,et al.  Imaging Profile of the COVID-19 Infection: Radiologic Findings and Literature Review , 2020, Radiology. Cardiothoracic imaging.

[9]  Lian-lian Wu,et al.  Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study , 2020, medRxiv.

[10]  Seong-Hoon Kim,et al.  X-ray Image Classification Using Random Forests with Local Wavelet-Based CS-Local Binary Patterns , 2011, Journal of Digital Imaging.

[11]  Gwo-Hshiung Tzeng,et al.  Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS , 2004, Eur. J. Oper. Res..

[12]  Dalila B.M.M. Fontes,et al.  Multicriteria Decision Making: A Case Study in the Automobile Industry , 2013 .

[13]  Kevin A. Schneider,et al.  Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach , 2020, Biocybernetics and Biomedical Engineering.

[14]  Begoña García Zapirain,et al.  Algorithms Based on CWT and Classifiers to Control Cardiac Alterations and Stress Using an ECG and a SCR , 2013, Sensors.

[15]  A. A. Zaidan,et al.  An evaluation and selection problems of OSS-LMS packages , 2016, SpringerPlus.

[16]  Prateek Chhikara,et al.  Deep Convolutional Neural Network with Transfer Learning for Detecting Pneumonia on Chest X-Rays , 2019, Advances in Intelligent Systems and Computing.

[17]  Mazin Abed Mohammed,et al.  A Review of Fog Computing and Machine Learning: Concepts, Applications, Challenges, and Open Issues , 2019, IEEE Access.

[18]  A. A. Zaidan,et al.  A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: coherent taxonomy, open issues and recommendation pathway solution , 2018, Health and Technology.

[19]  Yunna Wu,et al.  Sustainability performance assessment of wind power coupling hydrogen storage projects using a hybrid evaluation technique based on interval type-2 fuzzy set , 2019, Energy.

[20]  N. Arunkumar,et al.  Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques , 2018, Neural Computing and Applications.

[21]  Pi-Fang Hsu,et al.  Optimizing the Information Outsourcing Practices of Primary Care Medical Organizations Using Entropy and TOPSIS , 2008 .

[22]  Timothy C. Coburn,et al.  GIS and Multicriteria Decision Analysis , 2000 .

[23]  R. Lu,et al.  Detection of SARS-CoV-2 in Different Types of Clinical Specimens. , 2020, JAMA.

[24]  Alexander Wong,et al.  COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images , 2020, Scientific reports.

[25]  Nureize Arbaiy,et al.  A Novel Multi-Perspective Benchmarking Framework for Selecting Image Dehazing Intelligent Algorithms Based on BWM and Group VIKOR Techniques , 2020, Int. J. Inf. Technol. Decis. Mak..

[26]  Belal Al-Khateeb,et al.  Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images , 2018, Comput. Electr. Eng..

[27]  Wenyu Liu,et al.  Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label , 2020, medRxiv.

[28]  J. Rezaei.  Best-worst multi-criteria decision-making method , 2015 .

[29]  T. Miranda Lakshmi,et al.  A Survey on Multi Criteria Decision Making Methods and Its Applications , 2013 .

[30]  Bo Xu,et al.  A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19) , 2020, European Radiology.

[31]  Ting Zhang,et al.  Evaluation of renewable power sources using a fuzzy MCDM based on cumulative prospect theory: A case in China , 2018 .

[32]  Yicheng Fang,et al.  Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR , 2020, Radiology.

[33]  A. A. Zaidan,et al.  Comprehensive insights into evaluation and benchmarking of real-time skin detectors: Review, open issues & challenges, and recommended solutions , 2018 .

[34]  Mazin Abed Mohammed,et al.  Solving vehicle routing problem by using improved K-nearest neighbor algorithm for best solution , 2017, J. Comput. Sci..

[35]  Q. Tao,et al.  Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases , 2020, Radiology.

[36]  Sen Guo,et al.  Fuzzy best-worst multi-criteria decision-making method and its applications , 2017, Knowl. Based Syst..

[37]  Begonya Garcia-Zapirain,et al.  Automatic migraine classification via feature selection committee and machine learning techniques over imaging and questionnaire data , 2017, BMC Medical Informatics and Decision Making.

[38]  B. B. Zaidan,et al.  Novel Methodology for Triage and Prioritizing Using "Big Data" Patients with Chronic Heart Diseases Through Telemedicine Environmental , 2017, Int. J. Inf. Technol. Decis. Mak..

[39]  R. L. Keeney,et al.  Decisions with Multiple Objectives: Preferences and Value Trade-Offs , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[40]  B. B. Zaidan,et al.  Robust Pornography Classification Solving the Image Size Variation Problem Based on Multi-Agent Learning , 2015, J. Circuits Syst. Comput..

[41]  İhsan Kaya,et al.  Use of MCDM techniques for energy policy and decision‐making problems: A review , 2018 .

[42]  Chung-Hsing Yeh,et al.  Inter-company comparison using modified TOPSIS with objective weights , 2000, Comput. Oper. Res..

[43]  Kilian M. Pohl,et al.  Computing group cardinality constraint solutions for logistic regression problems , 2017, Medical Image Anal..

[44]  Hayit Greenspan,et al.  Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis , 2020, ArXiv.

[45]  Zoltán Kaló,et al.  Multiple Criteria Decision Analysis for Health Care Decision Making--Emerging Good Practices: Report 2 of the ISPOR MCDA Emerging Good Practices Task Force. , 2016, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[46]  Stanley Zionts,et al.  MCDM---If Not a Roman Numeral, Then What? , 1979 .

[47]  R. Baltussen,et al.  Priority setting of health interventions: the need for multi-criteria decision analysis , 2006, Cost effectiveness and resource allocation : C/E.

[48]  Xiaowei Xu,et al.  A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia , 2020, Engineering.

[49]  James K. C. Chen,et al.  Adopting information systems for China enterprise investing using the Grey Relation Analysis , 2007 .

[50]  Ajith Abraham,et al.  Hybrid fuzzy-linear programming approach for multi-criteria decision making problems , 2003, Neural Parallel Sci. Comput..

[51]  Aida Mustapha,et al.  Evaluating the Performance of Three Classification Methods in Diagnosis of Parkinson's Disease , 2018, SCDM.

[52]  F. M. Jumaah,et al.  Decision-making solution based multi-measurement design parameter for optimization of GPS receiver tracking channels in static and dynamic real-time positioning multipath environment , 2018 .

[53]  Nor Badrul Anuar,et al.  Cloud Service Selection Using Multicriteria Decision Analysis , 2014, TheScientificWorldJournal.

[54]  Max A. Viergever,et al.  Adaptive Stochastic Gradient Descent Optimisation for Image Registration , 2009, International Journal of Computer Vision.

[55]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[56]  Antonio Criminisi.  Machine learning for medical images analysis , 2016, Medical Image Anal..

[57]  L. Mombaerts,et al.  A machine learning-based model for survival prediction in patients with severe COVID-19 infection , 2020, medRxiv.

[58]  K. Cao,et al.  Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT , 2020, Radiology.

[59]  Rajendra M. Sonar,et al.  Analytic Hierarchy Process (AHP), Weighted Scoring Method (WSM), and Hybrid Knowledge Based System (HKBS) for Software Selection: A Comparative Study , 2009, 2009 Second International Conference on Emerging Trends in Engineering & Technology.

[60]  Norliza Katuk,et al.  On Multi Attribute Decision Making Methods: Prioritizing Information Security Controls , 2014 .