Symptoms Prediction of Tuberculosis using Soft Computing Technique

Abstract

It has been challenging to conduct research on the likelihood and consequences of TB survival. Significant advancements have been accomplished in a few linked domains from the initial phases of the associated study. For instance, improvements in biomedicine have raised estimations and records for prognostic aspects. Cheap computer hardware and software can also deliver better information, and data has been evaluated using a number of analytics techniques. One of the most common diseases and the leading cause of death in developed countries like India is tuberculosis. In recent years, the high prevalence of tuberculosis among all people has increased. In this work, we have discussed different types of Tuberculosis (TB) and, using Rough Set Theory (RST), find that Pulmonary TB is the most alarming in our state Odisha. We then active dataset of active cases of the Pulmonary dataset to generate a set of rules using Rough Set Theory (RST). We validate our claim by using a statistical method using, attest.