About: Main factors influencing recovery in MERS Co-V patients using machine learning

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Main factors influencing recovery in MERS Co-V patients using machine learning
Creator	John, Maya Shaiba, Hadil
Source	Elsevier; Medline; PMC
abstract	Abstract Background Middle East Respiratory Syndrome (MERS) is a major infectious disease which has affected the Middle Eastern countries, especially the Kingdom of Saudi Arabia (KSA) since 2012. The high mortality rate associated with this disease has been a major cause of concern. This paper aims at identifying the major factors influencing MERS recovery in KSA. Methods The data used for analysis was collected from the Ministry of Health website, KSA. The important factors impelling the recovery are found using machine learning. Machine learning models such as support vector machine, conditional inference tree, naïve Bayes and J48 are modelled to identify the important factors. Univariate and multivariate logistic regression analysis is also carried out to identify the significant factors statistically. Result The main factors influencing MERS recovery rate are identified as age, pre-existing diseases, severity of disease and whether the patient is a healthcare worker or not. In spite of MERS being a zoonotic disease, contact with camels is not a major factor influencing recovery. Conclusion The methods used were able to determine the prime factors influencing MERS recovery. It can be comprehended that awareness about symptoms and seeking medical intervention at the onset of development of symptoms will make a long way in reducing the mortality rate.
has issue date	2019-10-31(xsd:dateTime)
bibo:doi	10.1016/j.jiph.2019.03.020
bibo:pmid	30979679
has license	els-covid
sha1sum (hex)	fc5352a50f90e04d8c5ed64243f863417133e4dc
schema:url	https://doi.org/10.1016/j.jiph.2019.03.020
resource representing a document's title	Main factors influencing recovery in MERS Co-V patients using machine learning
has PubMed Central identifier	PMC7102802
has PubMed identifier	30979679
schema:publication	Journal of Infection and Public Health
resource representing a document's body	covid:fc5352a50f90e04d8c5ed64243f863417133e4dc#body_text
is schema:about of	named entity 'MERS' named entity 'seeking' named entity 'influencing' named entity 'Univariate' named entity 'Main' named entity 'PATIENTS' named entity 'USING' named entity 'CONDITIONAL' named entity 'BEING' named entity 'DISEASE' named entity 'ANALYSIS' named entity 'USED' named entity 'WAY' named entity 'MAKE' named entity 'IS A' named entity 'CAMELS' named entity 'SEVERITY' covid:arg/fc5352a50f90e04d8c5ed64243f863417133e4dc named entity 'MIDDLE EAST RESPIRATORY SYNDROME ' named entity 'FOUND' named entity 'KINGDOM OF SAUDI ARABIA' named entity 'HEALTHCARE WORKER' named entity 'SYMPTOMS' named entity 'SIGNIFICANT' named entity 'COLLECTED' named entity 'IDENTIFYING' named entity 'PRIME' named entity 'MODELS' named entity 'AGE' named entity 'MAIN' named entity 'SUPPORT VECTOR MACHINE' named entity 'RECOVERY' named entity 'INFERENCE' named entity 'TO IDENTIFY' named entity 'IDENTIFIED' named entity 'DATA' named entity 'MACHINE LEARNING' named entity 'MAIN' named entity 'CONCLUSION' named entity 'MULTIVARIATE LOGISTIC REGRESSION' named entity 'STATISTICALLY' named entity 'MAJOR' named entity 'MORTALITY RATE' named entity 'USING' named entity 'DISEASES' named entity 'MERS' named entity 'RECOVERY' named entity 'RESULT' named entity 'RATE' named entity 'AWARENESS' named entity 'LOGISTIC REGRESSION ANALYSIS' named entity 'METHODS' named entity 'PAPER' named entity 'DEVELOPMENT' named entity 'CONCERN' named entity 'ASSOCIATED WITH' named entity 'AIMS' named entity 'LONG' named entity 'PATIENT' named entity 'MEDICAL INTERVENTION' named entity 'MERS' named entity 'INFECTIOUS DISEASE' named entity 'IMPORTANT' named entity 'COUNTRIES' named entity 'A MAJOR' named entity 'HEALTH' named entity 'HIGH' named entity 'BACKGROUND' named entity 'TREE' named entity 'PRE-EXISTING' named entity 'CAUSE' named entity 'AFFECTED' named entity 'MIDDLE EASTERN' named entity 'WEBSITE' named entity 'MACHINE LEARNING' named entity 'CONTACT WITH' named entity 'ABOUT' named entity 'CARRIED' named entity 'ONSET OF' named entity 'FACTOR'

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