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An Early Detection of Asthma using BOMLA Detector

Awal, MA, Hossain, MS, Debjit, K, Ahmed, N, Nath, RD, Monsur Habib, G, Khan, MS, Islam, MA and Mahmud, M A Parvez 2021, An Early Detection of Asthma using BOMLA Detector, IEEE Access, vol. 9, pp. 58403-58420, doi: 10.1109/ACCESS.2021.3073086.

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Title An Early Detection of Asthma using BOMLA Detector
Author(s) Awal, MA
Hossain, MS
Debjit, K
Ahmed, N
Nath, RD
Monsur Habib, G
Khan, MS
Islam, MA
Mahmud, M A ParvezORCID iD for Mahmud, M A Parvez orcid.org/0000-0002-1905-6800
Journal name IEEE Access
Volume number 9
Start page 58403
End page 58420
Total pages 18
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2021
ISSN 2169-3536
2169-3536
Keyword(s) Science & Technology
Technology
Computer Science, Information Systems
Engineering, Electrical & Electronic
Telecommunications
Computer Science
Engineering
Respiratory system
Detectors
Machine learning
Machine learning algorithms
Diseases
Static VAr compensators
Radio frequency
Classification
clinical and non-clinical data
asthma
ADASYN
ANOVA
Summary 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 ( B ayesian O ptimisation-based M achine L earning framework for A 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.
Language eng
DOI 10.1109/ACCESS.2021.3073086
Indigenous content off
Field of Research 08 Information and Computing Sciences
09 Engineering
10 Technology
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30150345

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.