Int J Med Sci 2014; 11(5):508-514. doi:10.7150/ijms.8249 This issue

Research Paper

Tuberculosis Disease Diagnosis Using Artificial Immune Recognition System

Shahaboddin Shamshirband 1✉, Somayeh Hessam2, Hossein Javidnia3, Mohsen Amiribesheli4, Shaghayegh Vahdat2, Dalibor Petković5, Abdullah Gani6, Miss Laiha Mat Kiah6

1. Department of Computer Science, Chalous Branch, Islamic Azad University (IAU), 46615-397 Chalous, Mazandaran, Iran;
2. Department of Health Services Administration, Science and Research Branch, Islamic Azad University, Shiraz Fars, Iran;
3. Department of Computer Engineering, University of Guilan, Iran.
4. Faculty of Science and Technology, Bournemouth University, UK.
5. University of Niš, Faculty of Mechanical Engineering, Department for Mechatronics and Control, Aleksandra Medvedeva 14, 18000 Niš, Serbia.
6. Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia.

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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

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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.