20 November 2018
Global reach, higher impact
Int J Med Sci 2014; 11(5):508-514. doi:10.7150/ijms.8249
Tuberculosis Disease Diagnosis Using Artificial Immune Recognition System
1. Department of Computer Science, Chalous Branch, Islamic Azad University (IAU), 46615-397 Chalous, Mazandaran, Iran;
Background: There is a high risk of tuberculosis (TB) disease diagnosis among conventional methods.
Objectives: This study is aimed at diagnosing TB using hybrid machine learning approaches.
Materials and Methods: Patient epicrisis reports obtained from the Pasteur Laboratory in the north of Iran were used. All 175 samples have twenty features. The features are classified based on incorporating a fuzzy logic controller and artificial immune recognition system. The features are normalized through a fuzzy rule based on a labeling system. The labeled features are categorized into normal and tuberculosis classes using the Artificial Immune Recognition Algorithm.
Results: Overall, the highest classification accuracy reached was for the 0.8 learning rate (α) values. The artificial immune recognition system (AIRS) classification approaches using fuzzy logic also yielded better diagnosis results in terms of detection accuracy compared to other empirical methods. Classification accuracy was 99.14%, sensitivity 87.00%, and specificity 86.12%.
Keywords: Artificial Immune Recognition System, Fuzzy system, Tuberculosis, Safety.
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How to cite this article:
Shamshirband S, Hessam S, Javidnia H, Amiribesheli M, Vahdat S, Petković D, Gani A, Kiah MLM. Tuberculosis Disease Diagnosis Using Artificial Immune Recognition System. Int J Med Sci 2014; 11(5):508-514. doi:10.7150/ijms.8249. Available from http://www.medsci.org/v11p0508.htm