The investigation of TB patients features with K-Means clustering

Firuzi Jahantigh, Farzad and Ameri, Hakimeh (2015) The investigation of TB patients features with K-Means clustering. Journal of Health and Biomedical Informatics, 2 (3). pp. 149-159. ISSN 2423-3498


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Introduction: According to the World Health Organization, TB is the largest cause of death among infectious diseases. Due to the high percentage of tuberculosis infection and the high number of death among these patients, this study was carried out to categorized and find the relationship between different clinical and demographical characteristics. Method: This descriptive analytical study was done on 600 patients from Masih Daneshvari hospital tuberculosis research center. K-means clustering, Apriori association rules, and data mining algorithms (SPSS Clementine software) were used for clustering and determining the common characteristics among patients. Results: Based on DUNN index, 3 clusters were chosen as optimal cluster. The common factors between clusters have been described in details in findings section. According to the characteristics of each cluster, patients can be classified based on the effectiveness of various factors Conclusion: According to the results of this study, the most important identified factors by the use of clustering are Hemoglobin, age, sex, smoking, alcohol and Creatinine. Based on the association rules the highest rate of relationship is found between cough, weight loss, and ESR.

Item Type: Article
Uncontrolled Keywords: Tuberculosis, Clustering, Association rules, Data mining,
Subjects: R Medicine > R Medicine (General)
R Medicine > RZ Other systems of medicine
Depositing User: simin salehi nejad
Date Deposited: 21 Feb 2016 06:02
Last Modified: 21 Feb 2016 06:02

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