Abstract
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.
Highlights
Tuberculosis is an infectious disease caused by Mycobacterium tuberculosis
K-means clustering method ever be applied to identify subgroups of patients based on the response to the Patient - Physician Discordance Scales (PPDS),health status and clinical visits [5]
Application of k-means clustering in the data treatment of tuberculosis patients produce that k = 4 is the optimum k cluster.This was validated by the technique of shaving gene and Silhoutte coefficient
Summary
Tuberculosis is an infectious disease caused by Mycobacterium tuberculosis. Each year, the WHO estimates that 8.7 million new cases and 1.4 million died of tuberculosis cases. Efforts to control tuberculosis cases are implementing the DOTS strategy (Direct Observed Treatment Shortcourse) which has been implemented at the clinic or hospital within 6-9 months [2]. Clustering is applied to data tuberculosis patients in Ethiopia based on spatial data patient. The clustering results are used as material planning control program national tuberculosis can be more effective by identifying the group and target interventions [4]. K-means clustering method ever be applied to identify subgroups of patients based on the response to the Patient - Physician Discordance Scales (PPDS),health status and clinical visits [5]. The purpose of this research is to study how the k-means clustering applied to the treatment of tuberculosis patients data for 6-9 months. The clustering is expected to identify subgroups of patients based on patient characteristics and results of their examinations to treatment
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