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Iran Red Crescent Med J. 2015 Apr 25;17(4):e24557. doi: 10.5812/ircmj.17(4)2015.24557. eCollection 2015 Apr.

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

Author information

1
Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kula Lumpur, Malaysia.
2
Department of Computer Science, Chalous Branch, Islamic Azad University, Chalous, IR Iran.
3
Department of Electrical and Computer Engineering, Faculty of Engineering, University of Hormozgan, Bandar Abbas, IR Iran.
4
Gastroenterology and Liver Diseases Research Center, Shahid Beheshti University of Medical Sciences, Tehran, IR Iran.
5
Department of Intensive Care, Faculty of Medicine, Western University of Arad, Arad, Romania.

Abstract

BACKGROUND:

Tuberculosis (TB) is a major global health problem, which has been ranked as the second leading cause of death from an infectious disease worldwide. Diagnosis based on cultured specimens is the reference standard, however results take weeks to process. Scientists are looking for early detection strategies, which remain the cornerstone of tuberculosis control. Consequently there is a need to develop an expert system that helps medical professionals to accurately and quickly diagnose the disease. Artificial Immune Recognition System (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy.

OBJECTIVES:

In order to increase the classification accuracy of AIRS, this study introduces a new hybrid system that incorporates a support vector machine into AIRS for diagnosing tuberculosis.

PATIENTS AND METHODS:

Patient epacris reports obtained from the Pasteur laboratory of Iran were used as the benchmark data set, with the sample size of 175 (114 positive samples for TB and 60 samples in the negative group). The strategy of this study was to ensure representativeness, thus it was important to have an adequate number of instances for both TB and non-TB cases. The classification performance was measured through 10-fold cross-validation, Root Mean Squared Error (RMSE), sensitivity and specificity, Youden's Index, and Area Under the Curve (AUC). Statistical analysis was done using the Waikato Environment for Knowledge Analysis (WEKA), a machine learning program for windows.

RESULTS:

With an accuracy of 100%, sensitivity of 100%, specificity of 100%, Youden's Index of 1, Area Under the Curve of 1, and RMSE of 0, the proposed method was able to successfully classify tuberculosis patients.

CONCLUSIONS:

There have been many researches that aimed at diagnosing tuberculosis faster and more accurately. Our results described a model for diagnosing tuberculosis with 100% sensitivity and 100% specificity. This model can be used as an additional tool for experts in medicine to diagnose TBC more accurately and quickly.

KEYWORDS:

Artificial Intelligence; Classification; Data Mining; Expert Systems; Support Vector Machines

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