Open AccessIdentification of Tuberculosis Patient Characteristics Using K-Means Clustering
Author(s) -
Betha Nur Sari
Publication year - 2016
Publication title -
scientific journal of informatics/scientific journal of informatics
Language(s) - English
Resource type - Journals
eISSN - 2460-0040
pISSN - 2407-7658
DOI - 10.15294/sji.v3i2.7909
Subject(s) - tuberculosis , cluster analysis , pulmonary tuberculosis , medicine , mycobacterium tuberculosis , disease , silhouette , statistics , mathematics , computer science , artificial intelligence , pathology
In Indonesia, tuberculosis remains one of the major health problems unresolved. Indonesia is second ranked in the world as the country with the most tuberculosis cases. The purpose of this research is to study how K-means clustering applied to the treatment of tuberculosis patients data in order to identify the characteristics of tuberculosis patients. The results of K-means clustering validated by gene shaving and silhoutte coefficient. The experiment results indicate the optimum clusters value obtained from the K-mean clustering that has been validated by gene shaving and silhouette coefficient. K-means clustering divided four groups of tuberculosis patients based on their characteristics. There were divided at a category of disease (pulmonary TB, Extra Pulmonary TB and both), the age of the patient and the results of treatment of tuberculosis.