Tuberculosis disease diagnosis using artificial immune recognition system
Authors: Shamshirband, S., Hessam, S., Javidnia, H., Amiribesheli, M., Vahdat, S., Petkovič, D., Gani, A. and Laiha Mat Kiah, M.
Journal: International Journal of Medical Sciences
Volume: 11
Issue: 5
Pages: 508-514
ISSN: 1449-1907
DOI: 10.7150/ijms.8249
Abstract: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%. © Ivyspring International Publisher.
Source: Scopus
Tuberculosis disease diagnosis using artificial immune recognition system.
Authors: Shamshirband, S., Hessam, S., Javidnia, H., Amiribesheli, M., Vahdat, S., Petković, D., Gani, A. and Kiah, M.L.M.
Journal: Int J Med Sci
Volume: 11
Issue: 5
Pages: 508-514
eISSN: 1449-1907
DOI: 10.7150/ijms.8249
Abstract: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%.
Source: PubMed
Tuberculosis Disease Diagnosis Using Artificial Immune Recognition System
Authors: Shamshirband, S., Hessam, S., Javidnia, H., Amiribesheli, M., Vahdat, S., Petkovic, D., Gani, A. and Kiah, L.M.
Journal: INTERNATIONAL JOURNAL OF MEDICAL SCIENCES
Volume: 11
Issue: 5
Pages: 508-514
ISSN: 1449-1907
DOI: 10.7150/ijms.8249
Source: Web of Science (Lite)
Tuberculosis Disease Diagnosis Using Artificial Immune Recognition System
Authors: Shamshirband, Hessam, Javidnia, Amiribesheli, Vahdat, Petkovic, Gani and Mat Kiah
Journal: International Journal of Medical Sciences
Volume: 11
Pages: 508-514
Publisher: Int J Med Sci
Abstract: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%.
http://www.medsci.org/v11p0508.htm
Source: Manual
Preferred by: Mohsen Amiribesheli
Tuberculosis disease diagnosis using artificial immune recognition system.
Authors: Shamshirband, S., Hessam, S., Javidnia, H., Amiribesheli, M., Vahdat, S., Petković, D., Gani, A. and Kiah, M.L.M.
Journal: International journal of medical sciences
Volume: 11
Issue: 5
Pages: 508-514
eISSN: 1449-1907
ISSN: 1449-1907
DOI: 10.7150/ijms.8249
Abstract: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%.Source: Europe PubMed Central