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Building predictive models for MERS-CoV infections using data mining techniques
BACKGROUND: 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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
King Saud Bin Abdulaziz University for Health Sciences. Production and hosting by Elsevier Limited.
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7102847/ https://www.ncbi.nlm.nih.gov/pubmed/27641481 http://dx.doi.org/10.1016/j.jiph.2016.09.007 |
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author | Al-Turaiki, Isra Alshahrani, Mona Almutairi, Tahani |
author_facet | Al-Turaiki, Isra Alshahrani, Mona Almutairi, Tahani |
author_sort | Al-Turaiki, Isra |
collection | PubMed |
description | BACKGROUND: 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. METHOD: 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. RESULTS: 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%. CONCLUSION: 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. |
format | Online Article Text |
id | pubmed-7102847 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | King Saud Bin Abdulaziz University for Health Sciences. Production and hosting by Elsevier Limited. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71028472020-03-31 Building predictive models for MERS-CoV infections using data mining techniques Al-Turaiki, Isra Alshahrani, Mona Almutairi, Tahani J Infect Public Health Article BACKGROUND: 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. METHOD: 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. RESULTS: 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%. CONCLUSION: 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. King Saud Bin Abdulaziz University for Health Sciences. Production and hosting by Elsevier Limited. 2016 2016-09-15 /pmc/articles/PMC7102847/ /pubmed/27641481 http://dx.doi.org/10.1016/j.jiph.2016.09.007 Text en © 2016 King Saud Bin Abdulaziz University for Health Sciences. Production and hosting by Elsevier Limited. 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. |
spellingShingle | Article Al-Turaiki, Isra Alshahrani, Mona Almutairi, Tahani Building predictive models for MERS-CoV infections using data mining techniques |
title | Building predictive models for MERS-CoV infections using data mining techniques |
title_full | Building predictive models for MERS-CoV infections using data mining techniques |
title_fullStr | Building predictive models for MERS-CoV infections using data mining techniques |
title_full_unstemmed | Building predictive models for MERS-CoV infections using data mining techniques |
title_short | Building predictive models for MERS-CoV infections using data mining techniques |
title_sort | building predictive models for mers-cov infections using data mining techniques |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7102847/ https://www.ncbi.nlm.nih.gov/pubmed/27641481 http://dx.doi.org/10.1016/j.jiph.2016.09.007 |
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