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Tuberculosis, more commonly referred to as TB, is an infectious disease stemming from Mycobacterium tuberculosis. While it primarily impacts the lungs, TB can also affect other body areas. Given its severity and global impact, timely and accurate diagnosis is crucial for controlling its spread and improving patient outcomes.
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Medical management of tuberculosis (TB) patients involves a comprehensive approach that includes diagnosis, treatment, and monitoring. The specific strategies can vary depending on the type of tuberculosis (latent or active), the patient's overall health status, and other considerations.
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Tuberculosis, often called TB, is a contagious illness primarily caused by Mycobacterium tuberculosis. It mainly affects the lung parenchyma but can also impact other body parts.
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Diagnosing tuberculosis with a novel support vector machine-based artificial immune recognition system.

Mahmoud Reza Saybani1, Shahaboddin Shamshirband2, Shahram Golzari Hormozi3

  • 1Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kula Lumpur, Malaysia.

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A new hybrid system combining Artificial Immune Recognition System (AIRS) with support vector machines achieved 100% accuracy in diagnosing tuberculosis (TB). This advancement offers a faster, more accurate tool for early TB detection and control.

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Area of Science:

  • Medical diagnostics
  • Artificial intelligence in healthcare
  • Infectious disease research

Background:

  • Tuberculosis (TB) is a leading infectious cause of death globally.
  • Current TB diagnosis relies on slow culture-based methods, necessitating faster detection strategies.
  • Artificial Immune Recognition System (AIRS) shows potential for disease diagnosis but requires accuracy improvements.

Purpose of the Study:

  • To enhance the classification accuracy of AIRS for tuberculosis diagnosis.
  • To introduce a novel hybrid system integrating support vector machines (SVM) with AIRS.

Main Methods:

  • A hybrid AIRS-SVM model was developed for tuberculosis diagnosis.
  • Patient data from the Pasteur laboratory of Iran (175 samples) was utilized.
  • Performance was evaluated using 10-fold cross-validation, assessing accuracy, sensitivity, specificity, RMSE, Youden's Index, and AUC via WEKA software.

Main Results:

  • The hybrid system achieved perfect classification with 100% accuracy, sensitivity, and specificity.
  • Root Mean Squared Error (RMSE) was 0, indicating minimal error.
  • Youden's Index and Area Under the Curve (AUC) were both 1, signifying optimal performance.

Conclusions:

  • The developed hybrid AIRS-SVM model demonstrates exceptional efficacy in diagnosing tuberculosis.
  • Achieving 100% sensitivity and specificity, this model can significantly aid medical professionals.
  • This system offers a rapid and accurate diagnostic tool, crucial for effective tuberculosis control.