A Mining Approach for Detection and Classification Techniques of Tuberculosis Diseases

A Correct diagnosis of Tuberculosis (TB) can be only stated by applying a medical test to patient's. The result of this test is obtained after a time period of about few days. The need of this study is to develop a Data Mining(DM) solution which makes diagnosis of tuberculosis as accurate as possible and helps deciding if it is reasonable to start tuberculosis treatment on suspected patients. In this research, we proposed the classification techniques to predict the existence of tuberculosis. we propose efficient Decision Tree algorithm technique approach for Tuberculosis prediction. Today medical field have come a long way to treat patients with various kind of diseases. Among the most threatening one is the Tuberculosis which cannot be observed with a naked eye and comes instantly when its limitations are reached. Bad clinical decisions would cause death of a patient which cannot be afforded by any hospital. The Decision Tree algorithm technique classifies the most usual types are: (i) Latent Tuberculosis,(ii) Active Tuberculosis. is an accurate and reliable method of tuberculosis patients. This study has contribution on forecasting patients before the medical tests.