ABSTRACT Tuberculosis (TB) is a disease caused by bacteria called Mycobacterium tuberculosis. It usually spreads through the air and attacks low immune bodies such as patients with Human Immunodeficiency Virus (HIV). This work focuses on finding close association rules, a promising technique in Data Mining, within TB data. The proposed method first normalizes of raw data from medical records which includes categorical, nominal and continuous attributes and then determines Association Rules from the normalized data with different support and confidence. Association rules are applied on a real data set containing medical records of patients with TB obtained from a state hospital. The rules determined describes close association between one symptom to another; as an example, like lihood that an occurrence of sputum is closely associated with blood cough and HIV. Keywords: Association Rule, Data Mining, Medical Diagnosis, Tuberculosis 1. INTRODUCTION Data Mining is an active research area. One of the most popular approaches to do data mining is discovering association rules
['T. Asha', 'S. Natarajan', 'K. N. B. Murthy']
Association-rule-based tuberculosis disease diagnosis