Tuberculosis disease diagnosis using artificial immune recognition system

There is a high risk of tuberculosis (TB) disease diagnosis among conventional methods. This study is aimed at diagnosing TB using hybrid machine learning approaches. Patient epicrisis reports obtained from the Pasteur Laboratory in the north of Iran were used. All 175 samples have twenty features....

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Published inInternational journal of medical sciences Vol. 11; no. 5; pp. 508 - 514
Main Authors Shamshirband, Shahaboddin, Hessam, Somayeh, Javidnia, Hossein, Amiribesheli, Mohsen, Vahdat, Shaghayegh, Petković, Dalibor, Gani, Abdullah, Kiah, Miss Laiha Mat
Format Journal Article
LanguageEnglish
Published Australia Ivyspring International Publisher 01.01.2014
Subjects
Algorithms
Artificial Intelligence
Fuzzy Logic
Humans
Immune System
Research Paper
Tuberculosis - diagnosis
Tuberculosis - immunology
Artificial Immune Recognition System
Fuzzy system
Tuberculosis
Safety
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Summary:There is a high risk of tuberculosis (TB) disease diagnosis among conventional methods. This study is aimed at diagnosing TB using hybrid machine learning approaches. 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. 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%.
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Competing Interests: The authors have declared that no competing interest exists.
ISSN:1449-1907
1449-1907
DOI:10.7150/ijms.8249