NDLI: Predicting the existence of mycobacterium tuberculosis infection by Bayesian Networks and Rough Sets

Content Provider	IEEE Xplore Digital Library
Author	Ucar, T. Karahoca, D. Karahoca, A.
Copyright Year	2010
Description	Author affiliation: Yazihm Mühendisligi Bölümü, Bahçe¿ehir Üniversitési (Ucar, T.; Karahoca, D.; Karahoca, A.)
Abstract	A correct diagnosis of tuberculosis can be only stated by applying a medical test to patient's phlegm. The result of this test is obtained after a time period of about 45 days. The purpose of this study is to develop a data mining solution which makes diagnosis of tuberculosis as accurate as possible and helps deciding if it is reasonable to start tuberculosis treatment on suspected patients without waiting the exact medical test results. In this research, we compared the use of Bayesian Networks and Rough Sets to predict the existence of mycobacterium tuberculosis. 503 different patient records having 30 separate input parameters are obtained from a private clinic and used in the entire process of this research. The Bayesian Network model classifies the instances with RMSE of 22% whereas Rough Set algorithm does the same classification with RMSE of 37%. As a result, Bayesian Network is an accurate and reliable method when compared with Rough Set method for classification of tuberculosis patients.
Starting Page	1
Ending Page	4
File Size	547572
Page Count	4
File Format	PDF
ISBN	9781424463800
e-ISBN	9781424463824
DOI	10.1109/BIYOMUT.2010.5479850
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-04-21
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Acquired immune deficiency syndrome Rough sets Medical treatment Medical tests Data mining Testing Immune system
Content Type	Text
Resource Type	Article

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