A Sustainable Approach to Asthma Diagnosis: Classification with Data Augmentation, Feature Selection, and Boosting Algorithm
Asthma is a diverse disease that affects over 300 million individuals globally. The prevalence of asthma has increased by 50% every decade since the 1960s, making it a serious global health issue. In addition to its associated high mortality, asthma generates large economic losses due to the degrada...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/14/7/723 |
| _version_ | 1797212803939958784 |
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| author | Zne-Jung Lee Ming-Ren Yang Bor-Jiunn Hwang |
| author_facet | Zne-Jung Lee Ming-Ren Yang Bor-Jiunn Hwang |
| author_sort | Zne-Jung Lee |
| collection | DOAJ |
| description | Asthma is a diverse disease that affects over 300 million individuals globally. The prevalence of asthma has increased by 50% every decade since the 1960s, making it a serious global health issue. In addition to its associated high mortality, asthma generates large economic losses due to the degradation of patients’ quality of life and the impairment of their physical fitness. Asthma research has evolved in recent years to fully analyze why certain diseases develop based on a variety of data and observations of patients’ performance. The advent of new techniques offers good opportunities and application prospects for the development of asthma diagnosis methods. Over the last few decades, techniques like data mining and machine learning have been utilized to diagnose asthma. Nevertheless, these traditional methods are unable to address all of the difficulties associated with improving a small dataset to increase its quantity, quality, and feature space complexity at the same time. In this study, we propose a sustainable approach to asthma diagnosis using advanced machine learning techniques. To be more specific, we use feature selection to find the most important features, data augmentation to improve the dataset’s resilience, and the extreme gradient boosting algorithm for classification. Data augmentation in the proposed method involves generating synthetic samples to increase the size of the training dataset, which is then utilized to enhance the training data initially. This could lessen the phenomenon of imbalanced data related to asthma. Then, to improve diagnosis accuracy and prioritize significant features, the extreme gradient boosting technique is used. The outcomes indicate that the proposed approach performs better in terms of diagnostic accuracy than current techniques. Furthermore, five essential features are extracted to help physicians diagnose asthma. |
| first_indexed | 2024-04-24T10:48:12Z |
| format | Article |
| id | doaj.art-c34471b39ca14d3da31ed60fc7fb2f4c |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-04-24T10:48:12Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-c34471b39ca14d3da31ed60fc7fb2f4c2024-04-12T13:16:47ZengMDPI AGDiagnostics2075-44182024-03-0114772310.3390/diagnostics14070723A Sustainable Approach to Asthma Diagnosis: Classification with Data Augmentation, Feature Selection, and Boosting AlgorithmZne-Jung Lee0Ming-Ren Yang1Bor-Jiunn Hwang2Department of Electronic and Information Engineering, School of Advanced Manufacturing, Fuzhou University, Quanzhou 362200, ChinaGraduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 235, TaiwanCollege of Information Science, Ming Chuan University, Taoyuan 333, TaiwanAsthma is a diverse disease that affects over 300 million individuals globally. The prevalence of asthma has increased by 50% every decade since the 1960s, making it a serious global health issue. In addition to its associated high mortality, asthma generates large economic losses due to the degradation of patients’ quality of life and the impairment of their physical fitness. Asthma research has evolved in recent years to fully analyze why certain diseases develop based on a variety of data and observations of patients’ performance. The advent of new techniques offers good opportunities and application prospects for the development of asthma diagnosis methods. Over the last few decades, techniques like data mining and machine learning have been utilized to diagnose asthma. Nevertheless, these traditional methods are unable to address all of the difficulties associated with improving a small dataset to increase its quantity, quality, and feature space complexity at the same time. In this study, we propose a sustainable approach to asthma diagnosis using advanced machine learning techniques. To be more specific, we use feature selection to find the most important features, data augmentation to improve the dataset’s resilience, and the extreme gradient boosting algorithm for classification. Data augmentation in the proposed method involves generating synthetic samples to increase the size of the training dataset, which is then utilized to enhance the training data initially. This could lessen the phenomenon of imbalanced data related to asthma. Then, to improve diagnosis accuracy and prioritize significant features, the extreme gradient boosting technique is used. The outcomes indicate that the proposed approach performs better in terms of diagnostic accuracy than current techniques. Furthermore, five essential features are extracted to help physicians diagnose asthma.https://www.mdpi.com/2075-4418/14/7/723asthmadata augmentationfeature selectionextreme gradient boosting algorithmgenerative adversarial networks |
| spellingShingle | Zne-Jung Lee Ming-Ren Yang Bor-Jiunn Hwang A Sustainable Approach to Asthma Diagnosis: Classification with Data Augmentation, Feature Selection, and Boosting Algorithm Diagnostics asthma data augmentation feature selection extreme gradient boosting algorithm generative adversarial networks |
| title | A Sustainable Approach to Asthma Diagnosis: Classification with Data Augmentation, Feature Selection, and Boosting Algorithm |
| title_full | A Sustainable Approach to Asthma Diagnosis: Classification with Data Augmentation, Feature Selection, and Boosting Algorithm |
| title_fullStr | A Sustainable Approach to Asthma Diagnosis: Classification with Data Augmentation, Feature Selection, and Boosting Algorithm |
| title_full_unstemmed | A Sustainable Approach to Asthma Diagnosis: Classification with Data Augmentation, Feature Selection, and Boosting Algorithm |
| title_short | A Sustainable Approach to Asthma Diagnosis: Classification with Data Augmentation, Feature Selection, and Boosting Algorithm |
| title_sort | sustainable approach to asthma diagnosis classification with data augmentation feature selection and boosting algorithm |
| topic | asthma data augmentation feature selection extreme gradient boosting algorithm generative adversarial networks |
| url | https://www.mdpi.com/2075-4418/14/7/723 |
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