Building predictive models for MERS-CoV infections using data mining techniques
Identifieur interne : 000E54 ( Pmc/Curation ); précédent : 000E53; suivant : 000E55Building predictive models for MERS-CoV infections using data mining techniques
Auteurs : Isra Al-Turaiki ; Mona Alshahrani ; Tahani AlmutairiSource :
- Journal of Infection and Public Health [ 1876-0341 ] ; 2016.
Abstract
Recently, the outbreak of MERS-CoV infections caused worldwide attention to Saudi Arabia. The novel virus belongs to the coronaviruses family, which is responsible for causing mild to moderate colds. The control and command center of Saudi Ministry of Health issues a daily report on MERS-CoV infection cases. The infection with MERS-CoV can lead to fatal complications, however little information is known about this novel virus. In this paper, we apply two data mining techniques in order to better understand the stability and the possibility of recovery from MERS-CoV infections.
The Naive Bayes classifier and J48 decision tree algorithm were used to build our models. The dataset used consists of 1082 records of cases reported between 2013 and 2015. In order to build our prediction models, we split the dataset into two groups. The first group combined recovery and death records. A new attribute was created to indicate the record type, such that the dataset can be used to predict the recovery from MERS-CoV. The second group contained the new case records to be used to predict the stability of the infection based on the current status attribute.
The resulting recovery models indicate that healthcare workers are more likely to survive. This could be due to the vaccinations that healthcare workers are required to get on regular basis. As for the stability models using J48, two attributes were found to be important for predicting stability: symptomatic and age. Old patients are at high risk of developing MERS-CoV complications. Finally, the performance of all the models was evaluated using three measures: accuracy, precision, and recall. In general, the accuracy of the models is between 53.6% and 71.58%.
We believe that the performance of the prediction models can be enhanced with the use of more patient data. As future work, we plan to directly contact hospitals in Riyadh in order to collect more information related to patients with MERS-CoV infections.
Url:
DOI: 10.1016/j.jiph.2016.09.007
PubMed: 27641481
PubMed Central: 7102847
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Building predictive models for MERS-CoV infections using data mining techniques</title><author><name sortKey="Al Turaiki, Isra" sort="Al Turaiki, Isra" uniqKey="Al Turaiki I" first="Isra" last="Al-Turaiki">Isra Al-Turaiki</name></author><author><name sortKey="Alshahrani, Mona" sort="Alshahrani, Mona" uniqKey="Alshahrani M" first="Mona" last="Alshahrani">Mona Alshahrani</name></author><author><name sortKey="Almutairi, Tahani" sort="Almutairi, Tahani" uniqKey="Almutairi T" first="Tahani" last="Almutairi">Tahani Almutairi</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">27641481</idno><idno type="pmc">7102847</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7102847</idno><idno type="RBID">PMC:7102847</idno><idno type="doi">10.1016/j.jiph.2016.09.007</idno><date when="2016">2016</date><idno type="wicri:Area/Pmc/Corpus">000E54</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000E54</idno><idno type="wicri:Area/Pmc/Curation">000E54</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000E54</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Building predictive models for MERS-CoV infections using data mining techniques</title><author><name sortKey="Al Turaiki, Isra" sort="Al Turaiki, Isra" uniqKey="Al Turaiki I" first="Isra" last="Al-Turaiki">Isra Al-Turaiki</name></author><author><name sortKey="Alshahrani, Mona" sort="Alshahrani, Mona" uniqKey="Alshahrani M" first="Mona" last="Alshahrani">Mona Alshahrani</name></author><author><name sortKey="Almutairi, Tahani" sort="Almutairi, Tahani" uniqKey="Almutairi T" first="Tahani" last="Almutairi">Tahani Almutairi</name></author></analytic><series><title level="j">Journal of Infection and Public Health</title><idno type="ISSN">1876-0341</idno><idno type="eISSN">1876-035X</idno><imprint><date when="2016">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><title>Summary</title><sec><title>Background</title><p>Recently, the outbreak of MERS-CoV infections caused worldwide attention to Saudi Arabia. The novel virus belongs to the coronaviruses family, which is responsible for causing mild to moderate colds. The control and command center of Saudi Ministry of Health issues a daily report on MERS-CoV infection cases. The infection with MERS-CoV can lead to fatal complications, however little information is known about this novel virus. In this paper, we apply two data mining techniques in order to better understand the stability and the possibility of recovery from MERS-CoV infections.</p></sec><sec><title>Method</title><p>The Naive Bayes classifier and J48 decision tree algorithm were used to build our models. The dataset used consists of 1082 records of cases reported between 2013 and 2015. In order to build our prediction models, we split the dataset into two groups. The first group combined recovery and death records. A new attribute was created to indicate the record type, such that the dataset can be used to predict the recovery from MERS-CoV. The second group contained the new case records to be used to predict the stability of the infection based on the current status attribute.</p></sec><sec><title>Results</title><p>The resulting recovery models indicate that healthcare workers are more likely to survive. This could be due to the vaccinations that healthcare workers are required to get on regular basis. As for the stability models using J48, two attributes were found to be important for predicting stability: symptomatic and age. Old patients are at high risk of developing MERS-CoV complications. Finally, the performance of all the models was evaluated using three measures: accuracy, precision, and recall. In general, the accuracy of the models is between 53.6% and 71.58%.</p></sec><sec><title>Conclusion</title><p>We believe that the performance of the prediction models can be enhanced with the use of more patient data. As future work, we plan to directly contact hospitals in Riyadh in order to collect more information related to patients with MERS-CoV infections.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Han, J" uniqKey="Han J">J. Han</name></author><author><name sortKey="Kamber, M" uniqKey="Kamber M">M. Kamber</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Suh, S C" uniqKey="Suh S">S.C. Suh</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ferreira, D" uniqKey="Ferreira D">D. Ferreira</name></author><author><name sortKey="Oliveira, A" uniqKey="Oliveira A">A. Oliveira</name></author><author><name sortKey="Freitas, A" uniqKey="Freitas A">A. Freitas</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Venkatalakshmi, B" uniqKey="Venkatalakshmi B">B. Venkatalakshmi</name></author><author><name sortKey="Shivsankar, M" uniqKey="Shivsankar M">M. Shivsankar</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Majali, J" uniqKey="Majali J">J. Majali</name></author><author><name sortKey="Niranjan, R" uniqKey="Niranjan R">R. Niranjan</name></author><author><name sortKey="Phatak, V" uniqKey="Phatak V">V. Phatak</name></author><author><name sortKey="Tadakhe, O" uniqKey="Tadakhe O">O. Tadakhe</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bellaachia, A" uniqKey="Bellaachia A">A. Bellaachia</name></author><author><name sortKey="Guven, E" uniqKey="Guven E">E. Guven</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Afshar, H L" uniqKey="Afshar H">H.L. Afshar</name></author><author><name sortKey="Ahmadi, M" uniqKey="Ahmadi M">M. Ahmadi</name></author><author><name sortKey="Roudbari, M" uniqKey="Roudbari M">M. Roudbari</name></author><author><name sortKey="Sadoughi, F" uniqKey="Sadoughi F">F. Sadoughi</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Sandhu, R" uniqKey="Sandhu R">R. Sandhu</name></author><author><name sortKey="Sood, S K" uniqKey="Sood S">S.K. Sood</name></author><author><name sortKey="Kaur, G" uniqKey="Kaur G">G. Kaur</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Quinlan, R" uniqKey="Quinlan R">R. Quinlan</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Hall, M" uniqKey="Hall M">M. Hall</name></author><author><name sortKey="Frank, E" uniqKey="Frank E">E. Frank</name></author><author><name sortKey="Holmes, G" uniqKey="Holmes G">G. Holmes</name></author><author><name sortKey="Pfahringer, B" uniqKey="Pfahringer B">B. Pfahringer</name></author><author><name sortKey="Reutemann, P" uniqKey="Reutemann P">P. Reutemann</name></author><author><name sortKey="Wit Ten, I H" uniqKey="Wit Ten I">I.H. Wit-ten</name></author></analytic></biblStruct></listBibl></div1></back></TEI><pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">J Infect Public Health</journal-id><journal-id journal-id-type="iso-abbrev">J Infect Public Health</journal-id><journal-title-group><journal-title>Journal of Infection and Public Health</journal-title></journal-title-group><issn pub-type="ppub">1876-0341</issn><issn pub-type="epub">1876-035X</issn><publisher><publisher-name>King Saud Bin Abdulaziz University for Health Sciences. Production and hosting by Elsevier Limited.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">27641481</article-id><article-id pub-id-type="pmc">7102847</article-id><article-id pub-id-type="publisher-id">S1876-0341(16)30146-0</article-id><article-id pub-id-type="doi">10.1016/j.jiph.2016.09.007</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Building predictive models for MERS-CoV infections using data mining techniques</article-title></title-group><contrib-group><contrib contrib-type="author" id="aut0005"><name><surname>Al-Turaiki</surname><given-names>Isra</given-names></name><email>ialturaiki@ksu.edu.sa</email></contrib><contrib contrib-type="author" id="aut0010"><name><surname>Alshahrani</surname><given-names>Mona</given-names></name><email>monaalshahrani@outlook.com</email><xref rid="cor0005" ref-type="corresp">⁎</xref></contrib><contrib contrib-type="author" id="aut0015"><name><surname>Almutairi</surname><given-names>Tahani</given-names></name><email>435203979@student.ksu.edu.sa</email></contrib></contrib-group><aff id="aff0005">Information Technology Department, College of Computer and Information Sciences, King Saud University, Saudi Arabia</aff><author-notes><corresp id="cor0005"><label>⁎</label>Corresponding author. <email>monaalshahrani@outlook.com</email></corresp></author-notes><pub-date pub-type="pmc-release"><day>15</day><month>9</month><year>2016</year></pub-date><pmc-comment> PMC Release delay is 0 months and 0 days and was based on .</pmc-comment>
<pub-date pub-type="ppub" iso-8601-date="2016-12-01"><season>November-December</season><year>2016</year></pub-date><pub-date pub-type="epub"><day>15</day><month>9</month><year>2016</year></pub-date><volume>9</volume><issue>6</issue><fpage>744</fpage><lpage>748</lpage><history><date date-type="received"><day>23</day><month>6</month><year>2016</year></date><date date-type="rev-recd"><day>20</day><month>7</month><year>2016</year></date><date date-type="accepted"><day>6</day><month>9</month><year>2016</year></date></history><permissions><copyright-statement>© 2016 King Saud Bin Abdulaziz University for Health Sciences. Production and hosting by Elsevier Limited.</copyright-statement><copyright-year>2016</copyright-year><copyright-holder>King Saud Bin Abdulaziz University for Health Sciences</copyright-holder><license><license-p>Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.</license-p></license></permissions><abstract id="abs0005"><title>Summary</title><sec><title>Background</title><p>Recently, the outbreak of MERS-CoV infections caused worldwide attention to Saudi Arabia. The novel virus belongs to the coronaviruses family, which is responsible for causing mild to moderate colds. The control and command center of Saudi Ministry of Health issues a daily report on MERS-CoV infection cases. The infection with MERS-CoV can lead to fatal complications, however little information is known about this novel virus. In this paper, we apply two data mining techniques in order to better understand the stability and the possibility of recovery from MERS-CoV infections.</p></sec><sec><title>Method</title><p>The Naive Bayes classifier and J48 decision tree algorithm were used to build our models. The dataset used consists of 1082 records of cases reported between 2013 and 2015. In order to build our prediction models, we split the dataset into two groups. The first group combined recovery and death records. A new attribute was created to indicate the record type, such that the dataset can be used to predict the recovery from MERS-CoV. The second group contained the new case records to be used to predict the stability of the infection based on the current status attribute.</p></sec><sec><title>Results</title><p>The resulting recovery models indicate that healthcare workers are more likely to survive. This could be due to the vaccinations that healthcare workers are required to get on regular basis. As for the stability models using J48, two attributes were found to be important for predicting stability: symptomatic and age. Old patients are at high risk of developing MERS-CoV complications. Finally, the performance of all the models was evaluated using three measures: accuracy, precision, and recall. In general, the accuracy of the models is between 53.6% and 71.58%.</p></sec><sec><title>Conclusion</title><p>We believe that the performance of the prediction models can be enhanced with the use of more patient data. As future work, we plan to directly contact hospitals in Riyadh in order to collect more information related to patients with MERS-CoV infections.</p></sec></abstract><kwd-group id="kwd0005"><title>Keywords</title><kwd>MERS-CoV</kwd><kwd>Data mining</kwd><kwd>Decision tree</kwd><kwd>J48</kwd><kwd>Naive Bayes</kwd><kwd>Classification</kwd></kwd-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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