Feature Selection and Detection of COPD Using Automatic Programming Methods
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- @InProceedings{Karaca:2024:IDAP,
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author = "Huseyin Karaca and Sibel Arslan",
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title = "Feature Selection and Detection of {COPD} Using
Automatic Programming Methods",
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booktitle = "2024 8th International Artificial Intelligence and
Data Processing Symposium (IDAP)",
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year = "2024",
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month = sep,
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keywords = "genetic algorithms, genetic programming, Accuracy,
Automatic programming, Scalability, Lung, Predictive
models, Feature extraction, Chronic obstructive
pulmonary disease, Data models, Medical diagnosis,
Multigene Genetic Programming, Pulmonary Disease
Modelling",
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DOI = "
doi:10.1109/IDAP64064.2024.10710964",
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abstract = "Chronic obstructive pulmonary disease (COPD) is a
serious lung disease that severely limits patients'
quality of life and can lead to further health
complications if it is not diagnosed and treated in
time. In this study, various Automatic Programming (AP)
methods, including Genetic Programming (GP) and
Multi-Gene Genetic Programming (MGGP), are used to
achieve highly accurate predictions for diagnosis.
Among the methods, MGGP stands out with a prediction
accuracy of 100percent. The results highlight the
potential of AP methods in modelling complex nonlinear
relationships in COPD data and identifying key features
that influence the diagnosis of the disease. In
addition, the effectiveness and efficiency of AP
methods suggest that they can contribute to the
development of early diagnosis and treatment
strategies.",
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notes = "Also known as \cite{10710964}",
- }
Genetic Programming entries for
Huseyin Karaca
Sibel Arslan
Citations