ArticleInternational journal of medical sciences2014
Tuberculosis disease diagnosis using artificial immune recognition system.
Article in International journal of medical sciences, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 46 citations in OpenAlex.
- Machine learning based tuberculosis (ML-TB) health predictor model: early TB health disease prediction with ML models for prevention in developing countries.PeerJ. Computer science · 2024Article
- Supervised Machine Learning Methods for Seasonal Influenza Diagnosis.Diagnostics (Basel, Switzerland) · 2023Article
- Machine Learning-Based Differentiation of Nontuberculous Mycobacteria Lung Disease and Pulmonary Tuberculosis Using CT Images.BioMed research international · 2020Article
- Accuracy of an automated system for tuberculosis detection on chest radiographs in high-risk screening.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2018Article
- RAIRS2 a new expert system for diagnosing tuberculosis with real-world tournament selection mechanism inside artificial immune recognition system.Medical & biological engineering & computing · 2016Article
- Diagnosing tuberculosis with a novel support vector machine-based artificial immune recognition system.Iranian Red Crescent medical journal · 2015Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere is a high risk of tuberculosis (TB) disease diagnosis among conventional methods.
objectivesThis study is aimed at diagnosing TB using hybrid machine learning approaches. MATERIALS AND
methodsPatient 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.
resultsOverall, 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.