  In this paper, a methodology for the automated detection and classification
of Tuberculosis(TB) is presented. Tuberculosis is a disease caused by
mycobacterium which spreads through the air and attacks low immune bodies
easily. Our methodology is based on clustering and classification that
classifies TB into two categories, Pulmonary Tuberculosis(PTB) and retroviral
PTB(RPTB) that is those with Human Immunodeficiency Virus (HIV) infection.
Initially K-means clustering is used to group the TB data into two clusters and
assigns classes to clusters. Subsequently multiple different classification
algorithms are trained on the result set to build the final classifier model
based on K-fold cross validation method. This methodology is evaluated using
700 raw TB data obtained from a city hospital. The best obtained accuracy was
98.7% from support vector machine (SVM) compared to other classifiers. The
proposed approach helps doctors in their diagnosis decisions and also in their
treatment planning procedures for different categories.
