Selecting Accurate Classifier Models for a MERS-CoV Dataset
Identifieur interne : 000811 ( Main/Exploration ); précédent : 000810; suivant : 000812Selecting Accurate Classifier Models for a MERS-CoV Dataset
Auteurs :Source :
- Intelligent Systems and Applications ; 2018.
Abstract
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.
Url:
DOI: 10.1007/978-3-030-01054-6_74
PubMed: NONE
PubMed Central: 7123473
Affiliations:
Links toward previous steps (curation, corpus...)
- to stream Pmc, to step Corpus: 000195
- to stream Pmc, to step Curation: 000195
- to stream Pmc, to step Checkpoint: 000467
- to stream Ncbi, to step Merge: 002D18
- to stream Ncbi, to step Curation: 002D18
- to stream Ncbi, to step Checkpoint: 002D18
- to stream Main, to step Merge: 000814
- to stream Main, to step Curation: 000811
Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Selecting Accurate Classifier Models for a MERS-CoV Dataset</title>
</titleStmt>
<publicationStmt><idno type="wicri:source">PMC</idno>
<idno type="pmc">7123473</idno>
<idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7123473</idno>
<idno type="RBID">PMC:7123473</idno>
<idno type="doi">10.1007/978-3-030-01054-6_74</idno>
<idno type="pmid">NONE</idno>
<date when="2018">2018</date>
<idno type="wicri:Area/Pmc/Corpus">000195</idno>
<idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000195</idno>
<idno type="wicri:Area/Pmc/Curation">000195</idno>
<idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000195</idno>
<idno type="wicri:Area/Pmc/Checkpoint">000467</idno>
<idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000467</idno>
<idno type="wicri:Area/Ncbi/Merge">002D18</idno>
<idno type="wicri:Area/Ncbi/Curation">002D18</idno>
<idno type="wicri:Area/Ncbi/Checkpoint">002D18</idno>
<idno type="wicri:Area/Main/Merge">000814</idno>
<idno type="wicri:Area/Main/Curation">000811</idno>
<idno type="wicri:Area/Main/Exploration">000811</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Selecting Accurate Classifier Models for a MERS-CoV Dataset</title>
</analytic>
<series><title level="j">Intelligent Systems and Applications</title>
<imprint><date when="2018">2018</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc><textClass></textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en"><p id="Par1">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.</p>
</div>
</front>
<back><div1 type="bibliography"><listBibl><biblStruct></biblStruct>
<biblStruct></biblStruct>
<biblStruct><analytic><author><name sortKey="Koh, Hc" uniqKey="Koh H">HC Koh</name>
</author>
<author><name sortKey="Tan, G" uniqKey="Tan G">G Tan</name>
</author>
</analytic>
</biblStruct>
<biblStruct></biblStruct>
<biblStruct><analytic><author><name sortKey="Yoo" uniqKey="Yoo">Yoo</name>
</author>
</analytic>
</biblStruct>
<biblStruct><analytic><author><name sortKey="Al Turaiki, Isra" uniqKey="Al Turaiki I">Isra Al-Turaiki</name>
</author>
<author><name sortKey="Alshahrani, Mona" uniqKey="Alshahrani M">Mona Alshahrani</name>
</author>
<author><name sortKey="Almutairi, Tahani" uniqKey="Almutairi T">Tahani Almutairi</name>
</author>
</analytic>
</biblStruct>
<biblStruct></biblStruct>
<biblStruct></biblStruct>
<biblStruct></biblStruct>
<biblStruct><analytic><author><name sortKey="Asri, H" uniqKey="Asri H">H Asri</name>
</author>
<author><name sortKey="Mousannif, H" uniqKey="Mousannif H">H Mousannif</name>
</author>
<author><name sortKey="Moatassime, Ha" uniqKey="Moatassime H">HA Moatassime</name>
</author>
<author><name sortKey="Noel, T" uniqKey="Noel T">T Noel</name>
</author>
</analytic>
</biblStruct>
<biblStruct><analytic><author><name sortKey="Li, J" uniqKey="Li J">J Li</name>
</author>
<author><name sortKey="Zhao, Z" uniqKey="Zhao Z">Z Zhao</name>
</author>
<author><name sortKey="Liu, Y" uniqKey="Liu Y">Y Liu</name>
</author>
<author><name sortKey="Cheng, Z" uniqKey="Cheng Z">Z Cheng</name>
</author>
<author><name sortKey="Wang, X" uniqKey="Wang X">X Wang</name>
</author>
</analytic>
</biblStruct>
<biblStruct></biblStruct>
<biblStruct></biblStruct>
<biblStruct></biblStruct>
<biblStruct><analytic><author><name sortKey="Sandhu, R" uniqKey="Sandhu R">R Sandhu</name>
</author>
<author><name sortKey="Sood, Sk" uniqKey="Sood S">SK Sood</name>
</author>
<author><name sortKey="Kaur, G" uniqKey="Kaur G">G Kaur</name>
</author>
</analytic>
</biblStruct>
<biblStruct><analytic><author><name sortKey="Jang, Seongpil" uniqKey="Jang S">Seongpil Jang</name>
</author>
<author><name sortKey="Lee, Seunghwan" uniqKey="Lee S">Seunghwan Lee</name>
</author>
<author><name sortKey="Choi, Seong Min" uniqKey="Choi S">Seong-Min Choi</name>
</author>
<author><name sortKey="Seo, Junwon" uniqKey="Seo J">Junwon Seo</name>
</author>
<author><name sortKey="Choi, Hunseok" uniqKey="Choi H">Hunseok Choi</name>
</author>
<author><name sortKey="Yoon, Taeseon" uniqKey="Yoon T">Taeseon Yoon</name>
</author>
</analytic>
</biblStruct>
<biblStruct></biblStruct>
<biblStruct><analytic><author><name sortKey="Witten, H" uniqKey="Witten H">H Witten</name>
</author>
<author><name sortKey="Frank, E" uniqKey="Frank E">E Frank</name>
</author>
<author><name sortKey="Hall, Ma" uniqKey="Hall M">MA Hall</name>
</author>
</analytic>
</biblStruct>
<biblStruct></biblStruct>
<biblStruct><analytic><author><name sortKey="Stehman, Sv" uniqKey="Stehman S">SV Stehman</name>
</author>
</analytic>
</biblStruct>
<biblStruct><analytic><author><name sortKey="Sokolova, M" uniqKey="Sokolova M">M Sokolova</name>
</author>
<author><name sortKey="Lapalme, G" uniqKey="Lapalme G">G Lapalme</name>
</author>
</analytic>
</biblStruct>
</listBibl>
</div1>
</back>
</TEI>
<affiliations><list></list>
<tree></tree>
</affiliations>
</record>
Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Sante/explor/MersV1/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000811 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 000811 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien |wiki= Sante |area= MersV1 |flux= Main |étape= Exploration |type= RBID |clé= PMC:7123473 |texte= Selecting Accurate Classifier Models for a MERS-CoV Dataset }}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/Main/Exploration/RBID.i -Sk "pubmed:NONE" \ | HfdSelect -Kh $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd \ | NlmPubMed2Wicri -a MersV1
![]() | This area was generated with Dilib version V0.6.33. | ![]() |