Study of the TB patients features using Data mining method

According to the World Health Organization, TB is the biggest cause of death among the infectious diseases. Due to the highpercentage of people with tuberculosis infection and the high number of death among these patients, this study aimed to categorizeand find the relationship between different clinical and demographic characteristics. The study was conducted on 600 patients fromMasih-e-Daneshvari tuberculosis research center. The K-Means clustering data mining algorithms and Apriori association ruleswith SPSS Clementine software are used to perform the categorization and determining common indicators among patients. 3clusters according to Dunn index were chosen as the optimal clusters. Common factors between clusters are provided in detail inthe findings section. According the results of this study the most important factors identified by the clustering include hemoglobin,age, sex, smoking, alcohol consumption and Creatinine. The C 5.0 tree has 97.6% accuracy. According the results of this study themost important factors identified are hemoglobin, age, sex, smoking, alcohol consumption and Creatinine. C 5.0 rules by 75%confidence are extracted.