Abstract: Tuberculosis (TB) is an infectious disease usually caused by the bacterium Mycobacterium tuberculosis (MTB). Tuberculosis generally affects the lungs, but can also affect other parts of the body. Most infections do not have symptoms, in which case it is known as latent tuberculosis. About 10% of latent infections progress to active disease which, if left untreated, kills about half of those infected. The classic symptoms of active TB are a chronic cough with blood-containing sputum, fever, night sweats, and weight loss. The historical term "consumption" came about due to the weight loss. Infection of other organs can cause a wide range of symptoms. In previous years TB classification has been done using various algorithms like color segmentation, thresholding, histogram equalization. The main objective of this research Data Mining analysis uses efficient techniques and statistical measures for analyzing the data to predict the possible causes for the health issues and its impact on individual patients. Enormous data mining techniques are available for analysing the outcome accurately. When Classification technique is used in conjunction with the clustering technique , it produces considerable improvement in learning the accuracy particularly in detecting the Outliers. The main objective of using K-Means algorithm is to find the common factors between tuberculosis patients. Clustering is a useful technique of data distribution and finding patterns in the data. In the end, results are being evaluated after classification and testing on the basis of performance parameter such as accuracy, recall, precision, false acceptance ratio, and false rejection ratio.
Keywords: Tuberculosis(TB), Data mining, Neural Network, K-Means Algorithm.