Feature Selection and Detection of COPD Using Automatic Programming Methods

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@InProceedings{Karaca:2024:IDAP,

• author = "Huseyin Karaca and Sibel Arslan",
• title = "Feature Selection and Detection of {COPD} Using Automatic Programming Methods",
• booktitle = "2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP)",
• year = "2024",
• month = sep,
• 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",
• DOI = "doi:10.1109/IDAP64064.2024.10710964",
• 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.",
• notes = "Also known as \cite{10710964}",

}

Genetic Programming entries for Huseyin Karaca Sibel Arslan

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