Version 1
: Received: 26 August 2024 / Approved: 26 August 2024 / Online: 27 August 2024 (16:36:44 CEST)
How to cite:
Gürkan Kuntalp, D.; Özcan, N.; Düzyel, O.; Kababulut, Y.; Kuntalp, M. A Comparative Study of Metaheuristic Feature Selection Algorithms for Respiratory Disease Classification. Preprints2024, 2024081909. https://doi.org/10.20944/preprints202408.1909.v1
Gürkan Kuntalp, D.; Özcan, N.; Düzyel, O.; Kababulut, Y.; Kuntalp, M. A Comparative Study of Metaheuristic Feature Selection Algorithms for Respiratory Disease Classification. Preprints 2024, 2024081909. https://doi.org/10.20944/preprints202408.1909.v1
Gürkan Kuntalp, D.; Özcan, N.; Düzyel, O.; Kababulut, Y.; Kuntalp, M. A Comparative Study of Metaheuristic Feature Selection Algorithms for Respiratory Disease Classification. Preprints2024, 2024081909. https://doi.org/10.20944/preprints202408.1909.v1
APA Style
Gürkan Kuntalp, D., Özcan, N., Düzyel, O., Kababulut, Y., & Kuntalp, M. (2024). A Comparative Study of Metaheuristic Feature Selection Algorithms for Respiratory Disease Classification. Preprints. https://doi.org/10.20944/preprints202408.1909.v1
Chicago/Turabian Style
Gürkan Kuntalp, D., Yasin Kababulut and Mehmet Kuntalp. 2024 "A Comparative Study of Metaheuristic Feature Selection Algorithms for Respiratory Disease Classification" Preprints. https://doi.org/10.20944/preprints202408.1909.v1
Abstract
Correct diagnosis and early treatment of respiratory diseases can significantly improve the health status of patients, reduce healthcare expences, and enhance quality of life. Therefore, there has been extensive interest in developing automatic respiratory disease detection systems. Most of these methods have recently been using machine and deep learning algorithms. The success of 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.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
