Diagnosing tuberculosis with a novel support vector machine-based artificial immune recognition system

Iranian Red Crescent Medical Journal

Volumn 17, Issue 4, 2015, Pages

  Saybani, Mahmoud Reza ^a   Shamshirband, Shahaboddin ^b   Hormozi, Shahram Golzari ^c   Wah, Teh Ying ^a   Aghabozorgi, Saeed ^a   Pourhoseingholi, Mohamad Amin ^d   Olariu, Teodora ^e  

^a UNIVERSITY OF MALAYA   (Malaysia)

^b ISLAMIC AZAD UNIVERSITY   (Iran)

^c UNIVERSITY OF HORMOZGAN   (Iran)

^d SHAHID BEHESHTI UNIVERSITY OF MEDICAL SCIENCES   (Iran)

^e VASILE GOLDIS WESTERN UNIVERSITY OF ARAD   (Romania)

Author keywords

Artificial intelligence; Classification; Data mining; Expert systems; Support vector machines

Indexed keywords

ALANINE AMINOTRANSFERASE; ALBUMIN; ALKALINE PHOSPHATASE; HEMOGLOBIN; LACTATE DEHYDROGENASE;

ALGORITHM; AREA UNDER THE CURVE; ARTICLE; ARTIFICIAL IMMUNE RECOGNITION SYSTEM; ARTIFICIAL INTELLIGENCE; CLASSIFICATION ALGORITHM; COMPUTER ASSISTED DIAGNOSIS; CONTROLLED STUDY; CROSS-SECTIONAL STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; ERYTHROCYTE SEDIMENTATION RATE; EXPERT SYSTEM; HUMAN; KERNEL METHOD; LYMPHOCYTE COUNT; NEUTROPHIL COUNT; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; THROMBOCYTE COUNT; TUBERCULOSIS; VALIDATION PROCESS; YOUDEN INDEX;

EID: 84961223381     PISSN: 20741804     EISSN: 20741812     Source Type: Journal    
DOI: 10.5812/ircmj.17(4)2015.24557     Document Type: Article

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