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What is MRSA

Big Data Governance and Perspectives in Knowledge Management
Methicillin-resistant Staphylococcus aureus
Published in Chapter:
Classification Techniques and Data Mining Tools Used in Medical Bioinformatics
Satish Kumar David (King Saud University, Saudi Arabia), Amr T. M. Saeb (King Saud University, Saudi Arabia), Mohamed Rafiullah (King Saud University, Saudi Arabia), and Khalid Rubeaan (King Saud University, Saudi Arabia)
Copyright: © 2019 |Pages: 22
DOI: 10.4018/978-1-5225-7077-6.ch005
Abstract
Increasing volumes of data with the increased availability information mandates the use of data mining techniques in order to gather useful information from the datasets. In this chapter, data mining techniques are described with a special emphasis on classification techniques as one important supervised learning technique. Bioinformatics tools in the field for medical applications especially in medical microbiology are discussed. This chapter presents WEKA software as a tool of choice to perform classification analysis for different kinds of available data. Uses of WEKA data mining tools for biological applications such as genomic analysis and for medical applications such as diabetes are discussed. Data mining offers novel tools for medical applications for infectious diseases; it can help in identifying the pathogen and analyzing the drug resistance pattern. For non-communicable diseases such as diabetes, it provides excellent data analysis options for analyzing large volumes of data from many clinical studies.
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Hotline for Exposure to Occupational Hazards
Methicillin-resistant Staphylococcus aureus (MRSA, often pronounced “mersa”) is a bacterium responsible for difficult-to-treat infections in humans. It may also be referred to as multiple-resistant Staphylococcus aureus or oxacillin-resistant Staphylococcus aureus (ORSA). MRSA is by definition a strain of Staphylococcus aureus that is resistant to a large group of antibiotics called the beta-lactams, which include the penicillins and the cephalosporins.
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Toward Eradicating All Diseases
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