How metaheuristic algorithms can help in feature selection for Alzheimer’s diagnosis
Feature selection is the process of picking the most effective feature among a considerable number of features in the dataset. However, choosing the best subset that gives a higher performance in classification is challenging. This study constructed and validated multiple metaheuristic algorithms to...
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| Format: | Article |
| Language: | English |
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Ayandegan Institute of Higher Education,
2023-06-01
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| Series: | International Journal of Research in Industrial Engineering |
| Subjects: | |
| Online Access: | https://www.riejournal.com/article_179094_33d6d76906b0d685166d0381a35c9e3b.pdf |
| _version_ | 1797630496694337536 |
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| author | Farzaneh Salami Ali Bozorgi-Amiri Reza Tavakkoli-Moghaddam |
| author_facet | Farzaneh Salami Ali Bozorgi-Amiri Reza Tavakkoli-Moghaddam |
| author_sort | Farzaneh Salami |
| collection | DOAJ |
| description | Feature selection is the process of picking the most effective feature among a considerable number of features in the dataset. However, choosing the best subset that gives a higher performance in classification is challenging. This study constructed and validated multiple metaheuristic algorithms to optimize Machine Learning (ML) models in diagnosing Alzheimer’s. This study aims to classify Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and Alzheimer’s by selecting the best features. The features include Freesurfer features extracted from Magnetic Resonance Imaging (MRI) images and clinical data. We have used well-known ML algorithms for classifying, and after that, we used multiple metaheuristic methods for feature selection and optimizing the objective function of the classification. We considered the objective function a macro-average F1 score because of the imbalanced data. Our procedure not only reduces the irreverent features but also increases the classification performance. Results showed that metaheuristic algorithms could improve the performance of ML methods in diagnosing Alzheimer’s by 20%. We found that classification performance can be significantly enhanced by using appropriate metaheuristic algorithms. Metaheuristic algorithms can help find the best features for medical classification problems, especially Alzheimer’s. |
| first_indexed | 2024-03-11T11:07:57Z |
| format | Article |
| id | doaj.art-e894f535f73d4ddb9aa0b2cd6a7c2789 |
| institution | Directory Open Access Journal |
| issn | 2783-1337 2717-2937 |
| language | English |
| last_indexed | 2024-03-11T11:07:57Z |
| publishDate | 2023-06-01 |
| publisher | Ayandegan Institute of Higher Education, |
| record_format | Article |
| series | International Journal of Research in Industrial Engineering |
| spelling | doaj.art-e894f535f73d4ddb9aa0b2cd6a7c27892023-11-12T12:06:25ZengAyandegan Institute of Higher Education,International Journal of Research in Industrial Engineering2783-13372717-29372023-06-0112219720410.22105/riej.2023.347524.1321179094How metaheuristic algorithms can help in feature selection for Alzheimer’s diagnosisFarzaneh Salami0Ali Bozorgi-Amiri1Reza Tavakkoli-Moghaddam2Department of Industrial Engineering, Alborz Campus, University of Tehran, Tehran, Iran.Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.Feature selection is the process of picking the most effective feature among a considerable number of features in the dataset. However, choosing the best subset that gives a higher performance in classification is challenging. This study constructed and validated multiple metaheuristic algorithms to optimize Machine Learning (ML) models in diagnosing Alzheimer’s. This study aims to classify Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and Alzheimer’s by selecting the best features. The features include Freesurfer features extracted from Magnetic Resonance Imaging (MRI) images and clinical data. We have used well-known ML algorithms for classifying, and after that, we used multiple metaheuristic methods for feature selection and optimizing the objective function of the classification. We considered the objective function a macro-average F1 score because of the imbalanced data. Our procedure not only reduces the irreverent features but also increases the classification performance. Results showed that metaheuristic algorithms could improve the performance of ML methods in diagnosing Alzheimer’s by 20%. We found that classification performance can be significantly enhanced by using appropriate metaheuristic algorithms. Metaheuristic algorithms can help find the best features for medical classification problems, especially Alzheimer’s.https://www.riejournal.com/article_179094_33d6d76906b0d685166d0381a35c9e3b.pdfmetaheuristic algorithmalzheimer’s diseasemrimachine learningfeature selectiondata mining |
| spellingShingle | Farzaneh Salami Ali Bozorgi-Amiri Reza Tavakkoli-Moghaddam How metaheuristic algorithms can help in feature selection for Alzheimer’s diagnosis International Journal of Research in Industrial Engineering metaheuristic algorithm alzheimer’s disease mri machine learning feature selection data mining |
| title | How metaheuristic algorithms can help in feature selection for Alzheimer’s diagnosis |
| title_full | How metaheuristic algorithms can help in feature selection for Alzheimer’s diagnosis |
| title_fullStr | How metaheuristic algorithms can help in feature selection for Alzheimer’s diagnosis |
| title_full_unstemmed | How metaheuristic algorithms can help in feature selection for Alzheimer’s diagnosis |
| title_short | How metaheuristic algorithms can help in feature selection for Alzheimer’s diagnosis |
| title_sort | how metaheuristic algorithms can help in feature selection for alzheimer s diagnosis |
| topic | metaheuristic algorithm alzheimer’s disease mri machine learning feature selection data mining |
| url | https://www.riejournal.com/article_179094_33d6d76906b0d685166d0381a35c9e3b.pdf |
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