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Tuberculosis disease diagnosis using artificial immune recognition system.

Shahaboddin Shamshirband1, Somayeh Hessam2, Hossein Javidnia3

  • 11. Department of Computer Science, Chalous Branch, Islamic Azad University (IAU), 46615-397 Chalous, Mazandaran, Iran;

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Summary
This summary is machine-generated.

This study introduces a hybrid machine learning approach for tuberculosis (TB) diagnosis, achieving 99.14% accuracy. This method significantly improves upon conventional diagnostic techniques for TB.

Keywords:
Artificial Immune Recognition SystemFuzzy systemSafety.Tuberculosis

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Computational Biology

Background:

  • Conventional methods for diagnosing tuberculosis (TB) carry a high risk of misdiagnosis.
  • Accurate and timely TB diagnosis is crucial for effective treatment and public health.

Purpose of the Study:

  • To develop and evaluate a novel hybrid machine learning approach for improved tuberculosis diagnosis.
  • To leverage artificial immune systems and fuzzy logic for enhanced diagnostic accuracy.

Main Methods:

  • Utilized patient epicrisis reports from a laboratory in Iran, comprising 175 samples with twenty features.
  • Employed a hybrid model integrating a fuzzy logic controller with an artificial immune recognition system (AIRS).
  • Features were normalized using a fuzzy rule-based labeling system before classification by AIRS.

Main Results:

  • The hybrid model achieved a high classification accuracy of 99.14% with an optimal learning rate.
  • The artificial immune recognition system (AIRS) combined with fuzzy logic demonstrated superior diagnostic performance over empirical methods.
  • Achieved sensitivity of 87.00% and specificity of 86.12% in TB diagnosis.

Conclusions:

  • The proposed hybrid machine learning approach offers a highly accurate and effective method for tuberculosis diagnosis.
  • Integrating fuzzy logic with AIRS presents a promising advancement in medical diagnostic tools for infectious diseases.
  • This study highlights the potential of AI-driven solutions to overcome limitations in conventional TB diagnostic methods.