Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/14865
Title: A Comparative Study of Metaheuristic Feature Selection Algorithms for Respiratory Disease Classification
Authors: Kuntalp, Damla Gurkan
Ozcan, Nermin
Duzyel, Okan
Kababulut, Fevzi Yasin
Kuntalp, Mehmet
Keywords: metaheuristic
feature selection
respiratory disease classification
Publisher: Mdpi
Abstract: The correct diagnosis and early treatment of respiratory diseases can significantly improve the health status of patients, reduce healthcare expenses, and enhance quality of life. Therefore, there has been extensive interest in developing automatic respiratory disease detection systems. Most recent methods for detecting respiratory disease use machine and deep learning algorithms. The success of these machine learning methods depends heavily on the selection of proper features to be used in the classifier. Although metaheuristic-based feature selection methods have been successful in addressing difficulties presented by high-dimensional medical data in various biomedical classification tasks, there is not much research on the utilization of metaheuristic methods in respiratory disease classification. This paper aims to conduct a detailed and comparative analysis of six widely used metaheuristic optimization methods using eight different transfer functions in respiratory disease classification. For this purpose, two different classification cases were examined: binary and multi-class. The findings demonstrate that metaheuristic algorithms using correct transfer functions could effectively reduce data dimensionality while enhancing classification accuracy.
URI: https://doi.org/10.3390/diagnostics14192244
https://hdl.handle.net/11147/14865
ISSN: 2075-4418
Appears in Collections:PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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