Tuberculosis Disease Diagnosis Using Artificial Immune Recognition System
作者： Shahaboddin Shamshirband , Somayeh Hessam , Hossein Javidnia , Mohsen Amiribesheli , Shaghayegh Vahdat
DOI: 10.7150/IJMS.8249
关键词: Hybrid machine 、 Fuzzy rule 、 Fuzzy logic 、 Pattern recognition 、 Tuberculosis Disease 、 Artificial intelligence 、 Computer science 、 Immune recognition 、 Pathology 、 Fuzzy control system 、 Fuzzy logic controller 、 Tuberculosis
摘要: Background: There is a high risk of tuberculosis (TB) disease diagnosis among conventional methods. Objectives: This study aimed at diagnosing TB using hybrid machine learning approaches. Materials and Methods: Patient epicrisis reports obtained from the Pasteur Laboratory in north Iran were used. All 175 samples have twenty features. The features are classified based on incorporating fuzzy logic controller artificial immune recognition system. normalized through rule labeling labeled categorized into normal classes Artificial Immune Recognition Algorithm. Results: Overall, highest classification accuracy reached was for 0.8 rate (α) values. system (AIRS) approaches also yielded better results terms detection compared to other empirical Classification 99.14%, sensitivity 87.00%, specificity 86.12%.
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