Evaluation of TB Patients Characteristics Based on Predictive Data Mining Approaches

Abstract

According to the World Health Organization, Tb is the biggest cause of death among the infectious diseases. Due to the high percentage of people with tuberculosis infection and the high number of death among these patients, this study is a prospective study aimed to categorize and find the relationship between different clinical and demographic characteristics. The study was conducted on 600 patients from Masih-e-Daneshvari tuberculosis research center during 2015-2016. The K-Means clustering data mining algorithms and decision trees are used to perform the categorization and determine common indicators among patients. 2 clusters according to Dunn index were chosen as the optimal clusters. Common factors between clusters are provided in detail in the findings section. According to the results of this study, the most important factors identified by the clustering include hemoglobin, age, sex, smoking, alcohol consumption and creatinine. The RBF neural network tree has 98% accuracy. According to the results of this study, the most important factors identified are sex, smoking, alcohol consumption and WBC, albumin.

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