AI Article Synopsis
- The study addresses the challenges of diagnosing tuberculosis (TB) with conventional methods, highlighting the need for more effective approaches.
- Using hybrid machine learning methods, the researchers analyzed 175 patient reports from a laboratory in Iran, applying fuzzy logic and an artificial immune recognition system to categorize the data.
- Results showed that the hybrid approach significantly improved diagnosis accuracy, achieving 99.14% classification accuracy, with sensitivity at 87.00% and specificity at 86.12%.
Article Abstract
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%.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3970105 | PMC |
| http://dx.doi.org/10.7150/ijms.8249 | DOI Listing |
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