Main factors influencing recovery in MERS Co-V patients using machine learning | Zendy

Maya John | Zendy; Hadil Shaiba | Zendy

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

Author(s) -

Maya John,

Hadil Shaiba

Publication year - 2019

Publication title -

journal of infection and public health

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.983

H-Index - 35

eISSN - 1876-035X

pISSN - 1876-0341

DOI - 10.1016/j.jiph.2019.03.020

Subject(s) - logistic regression , univariate , disease , christian ministry , medicine , multivariate statistics , naive bayes classifier , mortality rate , environmental health , computer science , machine learning , support vector machine , philosophy , theology

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.

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