An Early Detection of Asthma Using BOMLA Detector
Asthma is a chronic and airway-induced disease, causing the incidence of bronchus inflammation, breathlessness, wheezing, is drastically becoming life-threatening. Even in the worst cases, it may destroy the quality to lead. Therefore, early detection of asthma is urgently needed, and machine learni...
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2021-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/9402730/ |
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| author | Md. Abdul Awal Md. Shahadat Hossain Kumar Debjit Nafiz Ahmed Rajan Dev Nath G. M. Monsur Habib Md. Salauddin Khan Md. Akhtarul Islam M. A. Parvez Mahmud |
| author_facet | Md. Abdul Awal Md. Shahadat Hossain Kumar Debjit Nafiz Ahmed Rajan Dev Nath G. M. Monsur Habib Md. Salauddin Khan Md. Akhtarul Islam M. A. Parvez Mahmud |
| author_sort | Md. Abdul Awal |
| collection | DOAJ |
| description | Asthma is a chronic and airway-induced disease, causing the incidence of bronchus inflammation, breathlessness, wheezing, is drastically becoming life-threatening. Even in the worst cases, it may destroy the quality to lead. Therefore, early detection of asthma is urgently needed, and machine learning can help identify asthma accurately. In this paper, a novel machine learning framework, namely BOMLA (<bold>B</bold>ayesian <bold>O</bold>ptimisation-based <bold>M</bold>achine <bold>L</bold>earning framework for <bold>A</bold>sthma) detector has been proposed to detect asthma. Ten classifiers have been utilized in the BOMLA detector, where Support Vector Classifier (SVC), Random Forest (RF), Gradient Boosting Classifier (GBC), eXtreme Gradient Boosting (XGB), and Artificial Neural Network (ANN) are state-of-the-art classifiers. In contrast, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QLDA), Naive Bayes (NB), Decision Tree (DT), and K-Nearest Neighbor (KNN) are conventional popular classifiers. ADASYN algorithm has also been employed in the BOMLA detector to eradicate the issues created due to the imbalanced dataset. It has even been attempted to delineate how the ADASYN algorithm affects the classification performance. The highest accuracy (ACC) and Matthews’s correlation coefficient (MCC) for an Asthma dataset provide 94.35% and 88.97%, respectively, using BOMLA detector when SVC is adapted, and it has been increased to 96.52% and 93.04%, respectively, when ensemble technique is adapted. The one-way analysis of variance (ANOVA) has also been performed in the 10-fold cross-validation to measure the statistical significance. A decision support system has been built as a potential application of the proposed system to visualize the probable outcome of the patient. Finally, it is expected that the BOMLA detector will help patients in their early diagnosis of asthma. |
| format | Article |
| id | doaj-art-3e92e6d0a22845dcb6f8b6a2162018a5 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3e92e6d0a22845dcb6f8b6a2162018a52025-08-25T23:00:46ZengIEEEIEEE Access2169-35362021-01-019584035842010.1109/ACCESS.2021.30730869402730An Early Detection of Asthma Using BOMLA DetectorMd. Abdul Awal0https://orcid.org/0000-0003-3028-4932Md. Shahadat Hossain1https://orcid.org/0000-0002-1368-7230Kumar Debjit2https://orcid.org/0000-0002-1779-0788Nafiz Ahmed3https://orcid.org/0000-0002-9445-2846Rajan Dev Nath4G. M. Monsur Habib5Md. Salauddin Khan6https://orcid.org/0000-0002-5001-7877Md. Akhtarul Islam7https://orcid.org/0000-0003-2396-2168M. A. Parvez Mahmud8https://orcid.org/0000-0002-1905-6800Electronics and Communication Engineering Discipline, Khulna University, Khulna, BangladeshDepartment of Quantitative Sciences, International University of Business Agriculture and Technology, Dhaka, BangladeshFaculty of Health, Engineering, and Sciences, University of Southern Queensland, Toowoomba, QLD, AustraliaFaculty of Law and Business, Peter Faber Business School, Australian Catholic University, North Sydney, NSW, AustraliaFaculty of Business, Education, Law and Arts, School of Commerce, University of Southern Queensland, Toowoomba, QLD, AustraliaGlobal Health Academy, The University of Edinburgh, Edinburgh, U.K.Statistics Discipline, Khulna University, Khulna, BangladeshStatistics Discipline, Khulna University, Khulna, BangladeshSchool of Engineering, Deakin University, Geelong, VIC, AustraliaAsthma is a chronic and airway-induced disease, causing the incidence of bronchus inflammation, breathlessness, wheezing, is drastically becoming life-threatening. Even in the worst cases, it may destroy the quality to lead. Therefore, early detection of asthma is urgently needed, and machine learning can help identify asthma accurately. In this paper, a novel machine learning framework, namely BOMLA (<bold>B</bold>ayesian <bold>O</bold>ptimisation-based <bold>M</bold>achine <bold>L</bold>earning framework for <bold>A</bold>sthma) detector has been proposed to detect asthma. Ten classifiers have been utilized in the BOMLA detector, where Support Vector Classifier (SVC), Random Forest (RF), Gradient Boosting Classifier (GBC), eXtreme Gradient Boosting (XGB), and Artificial Neural Network (ANN) are state-of-the-art classifiers. In contrast, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QLDA), Naive Bayes (NB), Decision Tree (DT), and K-Nearest Neighbor (KNN) are conventional popular classifiers. ADASYN algorithm has also been employed in the BOMLA detector to eradicate the issues created due to the imbalanced dataset. It has even been attempted to delineate how the ADASYN algorithm affects the classification performance. The highest accuracy (ACC) and Matthews’s correlation coefficient (MCC) for an Asthma dataset provide 94.35% and 88.97%, respectively, using BOMLA detector when SVC is adapted, and it has been increased to 96.52% and 93.04%, respectively, when ensemble technique is adapted. The one-way analysis of variance (ANOVA) has also been performed in the 10-fold cross-validation to measure the statistical significance. A decision support system has been built as a potential application of the proposed system to visualize the probable outcome of the patient. Finally, it is expected that the BOMLA detector will help patients in their early diagnosis of asthma.https://ieeexplore.ieee.org/document/9402730/Classificationclinical and non-clinical dataasthmaADASYNANOVA |
| spellingShingle | Md. Abdul Awal Md. Shahadat Hossain Kumar Debjit Nafiz Ahmed Rajan Dev Nath G. M. Monsur Habib Md. Salauddin Khan Md. Akhtarul Islam M. A. Parvez Mahmud An Early Detection of Asthma Using BOMLA Detector IEEE Access Classification clinical and non-clinical data asthma ADASYN ANOVA |
| title | An Early Detection of Asthma Using BOMLA Detector |
| title_full | An Early Detection of Asthma Using BOMLA Detector |
| title_fullStr | An Early Detection of Asthma Using BOMLA Detector |
| title_full_unstemmed | An Early Detection of Asthma Using BOMLA Detector |
| title_short | An Early Detection of Asthma Using BOMLA Detector |
| title_sort | early detection of asthma using bomla detector |
| topic | Classification clinical and non-clinical data asthma ADASYN ANOVA |
| url | https://ieeexplore.ieee.org/document/9402730/ |
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