Comparison between SARS CoV and MERS CoV Using Apriori Algorithm, Decision Tree, SVM

Jang, Seongpil; Lee, Seung Hwan; Choi, Seong-Min; Seo, Jun-Won; Choi, H. S.; Yoon, Taeseon

doi:10.1051/matecconf/20164908001

Cited by 14 publications

(17 citation statements)

References 12 publications

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“…The results showed that elderly people are more likely to be infected than others. In [15], an SVM classifier based on sigmoid, normal and polynomial iterations was used to analyse the MERS and SARS proteins. The results showed their behaviour similarity and approximate dissimilarities.…”

Section: Discussionmentioning

confidence: 99%

“…In [13], the sample size is represented by articles collected from the Internet and reported by 153 news media outlets in Korea and the comments associated with these articles from day 1 (first confirmed case on May 20, 2015) to the day 70. In [15], the dataset contains 322 records, 92 infected cases and 230 uninfected cases. In [16], synthetic data are generated for 0.2 million users.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, studies conducted on identifying the best model can help minimise the effects of epidemic diseases [11]. The distinction between SARS and MERS viruses can be considered a challenging task because of the similarities in their symptoms, such as breathing problems and high fever [15]. Medical diagnosis systems play a significant role in identifying patient health conditions with MERS-CoV symptoms [14].…”

Section: Motivationsmentioning

confidence: 99%

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Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review

et al. 2020

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Coronaviruses (CoVs) are a large family of viruses that are common in many animal species, including camels, cattle, cats and bats. Animal CoVs, such as Middle East respiratory syndrome-CoV, severe acute respiratory syndrome (SARS)-CoV, and the new virus named SARS-CoV-2, rarely infect and spread among humans. On January 30, 2020, the International Health Regulations Emergency Committee of the World Health Organisation declared the outbreak of the resulting disease from this new CoV called 'COVID-19', as a 'public health emergency of international concern'. This global pandemic has affected almost the whole planet and caused the death of more than 315,131 patients as of the date of this article. In this context, publishers, journals and researchers are urged to research different domains and stop the spread of this deadly virus. The increasing interest in developing artificial intelligence (AI) applications has addressed several medical problems. However, such applications remain insufficient given the high potential threat posed by this virus to global public health. This systematic review addresses automated AI applications based on data mining and machine learning (ML) algorithms for detecting and diagnosing COVID-19. We aimed to obtain an overview of this critical virus, address the limitations of utilising data mining and ML algorithms, and provide the health sector with the benefits of this technique. We used five databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus and performed three sequences of search queries between 2010 and 2020. Accurate exclusion criteria This article is part of the Topical Collection on Systems-Level Quality Improvement * A. A.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Motivationsmentioning

confidence: 99%

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Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review

et al. 2020

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“…In a study [48], to establish a disease prediction model, the Support Vector Machine (SVM), Decision Tree, and K-Nearest Neighbor (K-NN) classi cations were used on the MERS-CoV dataset, which included all reported cases in Saudi Arabia between 2013 and 2017. In a study [50], the researchers used three data mining algorithms of Apriori, Decision Tree, and SVM to compare and differentiate between two viruses with similar symptoms to SARS CoV and MERS CoV. They used the data of spike glycoprotein from NCBI.…”

Section: Data Miningmentioning

confidence: 99%

COVID-19 and information - communication technology: Common components in an interactive framework for predicting, preventing, controlling and monitoring the new COVID-19 disease

Yazdani

Sharifian

Ravangard

et al. 2020

Preprint

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BackgroundSince there is no specific treatment for coronavirus, there is an urgent need for global monitoring of people with Covid-19. The use of e-health services should be compatible with the diagnosis and control of the outbreak of zoonotic infectious diseases. The aim of this study is to provide a conceptual model based on health information technology services for Covid-19 disease management.MethodsThe present study is an applied descriptive study that was performed on a cross-sectional basis in a COVID-19 Center Hospital in Fars province of IRAN country in April 2020. The main tool of this research is a questionnaire that has been compiled by reviewing related articles in databases and surveying with experts in order to determine the necessary services in the management and control model of the prevalence of Covid19 disease. Then, in order to determine the necessary services in the conceptual model, this questionnaire was given to various specialists in the COVID-19 Center Hospital. Finally, based on the results of the questionnaire, a comprehensive conceptual model for the management and control of COVID 19 diseases is presented.ResultsThe proposed model consisted of three layers of cloud computing, fog and data acquisition. All services were approved by the surveyed participants. Among the services, Tele- monitoring for home quarantine, Electronic self-assessment, Telepsychology of patients in home and hospital quarantine, Tele prescription, Tele- information Tele- training have the highest agreement rate. proposed model is an integrated model. The innovation that can be mentioned in this research is the use of priority queue service as a service of the fog layer.ConclusionInformation communication technology tools have an important role in all aspects of contagious diseases management.

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“…A study [16] applies three data mining algorithms to compare two viruses with similar symptoms: severe acute respiratory syndrome (SARS) and MERS coronaviruses. Apriori, Decision Tree, and SVM data mining algorithms are used on data of the spike in glycoprotein from the NCBI to distinguish between the two viruses.…”

Section: Literature Reviewmentioning

confidence: 99%

Selecting Accurate Classifier Models for a MERS-CoV Dataset

Almoammar¹,

Alhenaki²,

Kurdi³

2018

Advances in Intelligent Systems and Computing

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The Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is a viral respiratory disease that is spreading worldwide necessitating to have an accurate diagnosis system that accurately predicts infections. As data mining classifiers can greatly assist in enhancing the prediction accuracy of diseases in general. In this paper, classifier model performance for two classification types:(1) binary and (2) multi-class were tested on a MERS-CoV dataset that consists of all reported cases in Saudi Arabia between 2013 and 2017. A cross-validation model was applied to measure the accuracy of the Support Vector Machine (SVM), Decision Tree, and k-Nearest Neighbor (k-NN) classifiers. Experimental results demonstrate that SVM and Decision Tree classifiers achieved the highest accuracy of 86.44% for binary classification based on healthcare personnel class. On the other hand, for multiclass classification based on city class, the decision tree classifier had the highest accuracy among the remaining classifiers; although it did not reach a satisfactory accuracy level (42.80%). This work is intended to be a part of a MERS-CoV prediction system to enhance the diagnosis of MERS-CoV disease.

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Comparison between SARS CoV and MERS CoV Using Apriori Algorithm, Decision Tree, SVM

Cited by 14 publications

References 12 publications

Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review

Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review

COVID-19 and information - communication technology: Common components in an interactive framework for predicting, preventing, controlling and monitoring the new COVID-19 disease

Selecting Accurate Classifier Models for a MERS-CoV Dataset

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