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A machine learning approach to differentiating bacterial from viral meningitis

Revett, Kenneth and Gorunescu, Florin and Gorunescu, Marina and Ene, Marius (2006) A machine learning approach to differentiating bacterial from viral meningitis. In: IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing (JVA'06). IEEE, Los Alamitos, USA, pp. 155-162. ISBN 0769526438


Official URL: http://dx.doi.org/10.1109/JVA.2006.2


Clinical reports indicate that differentiating bacterial from viral (aseptic) meningitis is still a difficult issue, compounded by factors such as age and time of presentation. Clinicians routinely rely on the results from blood and cerebrospinal fluid (CSF) to discriminate bacterial from viral meningitis. Tests such as the CSF Gram stain performed prior to broad-spectrum antibiotic treatment yield sensitivities between 60 and 92%. Sensitivity can be increased by performing additional laboratory testing, but the results are never completely accurate and are not cost effective in many cases. In this study, we wished to determine if a machine learning approach, based on rough sets and a probabilistic neural network could be used to differentiate between viral and bacterial meningitis. We analysed a clinical dataset containing records for 581 cases of acute bacterial or viral meningitis. The rough sets approach was used to perform dimensionality reduction in addition to classification. The results were validated using a probabilistic neural network. With an overall accuracy of 98%, these results indicate rough sets is a useful approach to differentiating bacterial from viral meningitis.

Item Type:Book Section
Uncontrolled Keywords:diseases learning (artificial intelligence) medical diagnostic computing neural nets probability rough set theory, gram stain, antibiotic treatment, automated diagnosis, bacterial meningitis, blood, cerebrospinal fluid, clinical dataset, dimensionality reduction, machine learning, probabilistic neural network, rough sets, viral meningitis
Research Community:University of Westminster > Electronics and Computer Science, School of
ID Code:4438
Deposited On:03 Sep 2007
Last Modified:11 Aug 2010 15:32

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