Tuberculosis Disease Diagnosis Using Artificial Immune Recognition System

This source preferred by Mohsen Amiribesheli

Authors: Shamshirband, Hessam, Javidnia, Amiribesheli, Vahdat, Petkovic, Gani and Mat Kiah

http://www.medsci.org/v11p0508.htm

Journal: International Journal of Medical Sciences

Volume: 11

Pages: 508-514

Publisher: Int J Med Sci

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%.

This data was imported from PubMed:

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

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%.

This data was imported from Scopus:

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

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.

This data was imported from Web of Science (Lite):

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

This data was imported from Europe PubMed Central:

Authors: Shamshirband, S., Hessam, S., Javidnia, H., Amiribesheli, M., Vahdat, S., Petković, D., Gani, A. and Kiah, M.L.

Journal: International journal of medical sciences

Volume: 11

Issue: 5

Pages: 508-514

eISSN: 1449-1907

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%.

The data on this page was last updated at 04:42 on November 17, 2017.