DocumentCode
1938967
Title
Notice of Retraction
Diagnosis of tuberculosis using ensemble methods
Author
Asha, T. ; Natarajan, Sriraam ; Murthy, K.N.B.
Author_Institution
Dept. of Comput. Sci., V.T.U., Bangalore, India
Volume
8
fYear
2010
fDate
9-11 July 2010
Firstpage
409
Lastpage
412
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Classification of medical data is an important task in the prediction of any disease. It even helps doctors in their diagnosis decisions. Ensemble classifier is to generate a set of classifiers instead of one classifier for the classification of a new object, hoping that the combination of answers of multiple classification results in better performance. Tuberculosis (TB) is a disease caused by bacteria called Mycobacterium tuberculosis. It usually spreads through the air & attacks low immune bodies. HIV patients are more likely to be attacked with TB. It is an important health problem in India also. Diagnosis of pulmonary tuberculosis has always been a problem. The main task carried out in this paper is the comparison of classification techniques for TB based on two categories namely pulmonary tuberculosis(PTB) and retroviral PTB using ensemble classifiers such as Bagging, AdaBoost and Random forest trees.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Classification of medical data is an important task in the prediction of any disease. It even helps doctors in their diagnosis decisions. Ensemble classifier is to generate a set of classifiers instead of one classifier for the classification of a new object, hoping that the combination of answers of multiple classification results in better performance. Tuberculosis (TB) is a disease caused by bacteria called Mycobacterium tuberculosis. It usually spreads through the air & attacks low immune bodies. HIV patients are more likely to be attacked with TB. It is an important health problem in India also. Diagnosis of pulmonary tuberculosis has always been a problem. The main task carried out in this paper is the comparison of classification techniques for TB based on two categories namely pulmonary tuberculosis(PTB) and retroviral PTB using ensemble classifiers such as Bagging, AdaBoost and Random forest trees.
Keywords
classification; diseases; medical information systems; patient diagnosis; Mycobacterium tuberculosis; disease; ensemble methods; medical data classification; pulmonary tuberculosis; tuberculosis diagnosis; Artificial neural networks; Bagging; Human immunodeficiency virus; Medical diagnostic imaging; Radiography; Ensemble classifiers; PTB; Tuberculosis; classification; retroviral PTB;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-5537-9
Type
conf
DOI
10.1109/ICCSIT.2010.5564025
Filename
5564025
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