Notice of RetractionDiagnosis of tuberculosis using ensemble methods

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.