{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T20:53:42Z","timestamp":1789419222300,"version":"build-2803163510"},"reference-count":46,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Cogninet Australia Pty Ltd under the Industry Research Scholarship"},{"name":"University of Southern Queensland International PhD Fee Scholarship 2022\u20132025"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background and objective: Chronic respiratory diseases, such as asthma and COPD, pose significant challenges to human health and global healthcare systems. This pioneering study utilises AI analysis and modelling of cough and respiratory sound signals to classify and differentiate between asthma, COPD, and healthy subjects. The aim is to develop an AI-based diagnostic system capable of accurately distinguishing these conditions, thereby enhancing early detection and clinical management. Our study, therefore, presents the first AI system that leverages dual acoustic signals to enhance the diagnostic ACC of asthma using automated, lightweight deep learning models. Methods: To build an automated, lightweight model for asthma detection, tested separately with respiratory and cough sounds to assess their suitability for detecting asthma and COPD, the proposed AI models integrate the following ML algorithms: RF, SVM, DT, NN, and KNN, with an overall aim to demonstrate the efficacy of the proposed method for future clinical use. Model training and validation were performed using 5-fold cross-validation, wherein the dataset was randomly divided into five folds and the models were trained and tested iteratively to ensure robust performance. We evaluated the model outcomes with several performance metrics: ACC, precision, recall, F1 score, and area under the AUC. Additionally, a majority voting ensemble technique was employed to aggregate the predictions of the various classifiers for improved diagnostic reliability. We applied Gabor time\u2013frequency transformation for feature extraction and NCA) for feature selection to optimise predictive accuracy. Independent comparative experiments were conducted, where cough-sound subsets were used to evaluate asthma detection capabilities, and respiratory-sound subsets were used to evaluate COPD detection capabilities, allowing for targeted model assessment. Results: The proposed ensemble approach, facilitated by a majority voting approach for model efficacy evaluation, achieved acceptable ACC values of 94.05% and 83.31% for differentiating between asthma and normal cases utilising separate respiratory sounds and cough sounds, respectively. The results highlight a substantial benefit in integrating multiple classifier models and sound modalities while demonstrating an unprecedented level of ACC and robustness for future diagnostic predictions of the disease. Conclusions: The present study sets a new benchmark in AI-based detection of respiratory diseases by integrating cough and respiratory sound signals for future diagnostics. The successful implementation of a dual-sound analysis approach promises advancements in the early detection and management of asthma and COPD.We conclude that the proposed model holds strong potential to transform asthma diagnostic practices and support clinicians in their respiratory healthcare practices.<\/jats:p>","DOI":"10.3390\/diagnostics15091155","type":"journal-article","created":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T06:49:02Z","timestamp":1746082142000},"page":"1155","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Automated Lightweight Model for Asthma Detection Using Respiratory and Cough Sound Signals"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8681-0332","authenticated-orcid":false,"given":"Shuting","family":"Xu","sequence":"first","affiliation":[{"name":"Artificial Intelligence Applications Laboratory, School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia"},{"name":"Cogninet Australia, Sydney, NSW 2010, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2290-6749","authenticated-orcid":false,"given":"Ravinesh C.","family":"Deo","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Applications Laboratory, School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3979-4077","authenticated-orcid":false,"given":"Oliver","family":"Faust","sequence":"additional","affiliation":[{"name":"School of Computing and Information Science Research, Anglia Ruskin University Cambridge Campus, East Rd, Cambridge CB1 1PT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1372-0042","authenticated-orcid":false,"given":"Prabal D.","family":"Barua","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Applications Laboratory, School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia"},{"name":"Cogninet Australia, Sydney, NSW 2010, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4964-7556","authenticated-orcid":false,"given":"Jeffrey","family":"Soar","sequence":"additional","affiliation":[{"name":"School of Business, University of Southern Queensland, Springfield, QLD 4350, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2689-8552","authenticated-orcid":false,"given":"Rajendra","family":"Acharya","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Applications Laboratory, School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1007\/s00408-020-00363-0","article-title":"Pulmonary embolism in acute asthma exacerbation: Clinical characteristics, prediction model and hospital outcomes","volume":"198","author":"Alzghoul","year":"2020","journal-title":"Lung"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1016\/j.acra.2018.10.022","article-title":"Machine learning algorithms utilizing functional respiratory imaging may predict COPD exacerbations","volume":"26","author":"Lanclus","year":"2019","journal-title":"Acad. Radiol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Abadi, M. (2016, January 18\u201324). TensorFlow: Learning functions at scale. Proceedings of the 21st ACM SIGPLAN International Conference on Functional Programming, Nara, Japan.","DOI":"10.1145\/2951913.2976746"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"103551","DOI":"10.1016\/j.compind.2021.103551","article-title":"Improved faster R-CNN for fabric defect detection based on Gabor filter with Genetic Algorithm optimization","volume":"134","author":"Chen","year":"2022","journal-title":"Comput. Ind."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.ijmedinf.2019.03.017","article-title":"Smooth Bayesian network model for the prediction of future high-cost patients with COPD","volume":"126","author":"Lin","year":"2019","journal-title":"Int. J. Med. Inform."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"16857","DOI":"10.1007\/s00521-024-09895-5","article-title":"Automated asthma detection in a 1326-subject cohort using a one-dimensional attractive-and-repulsive center-symmetric local binary pattern technique with cough sounds","volume":"36","author":"Barua","year":"2024","journal-title":"Neural Comput. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"127","DOI":"10.2214\/AJR.17.19229","article-title":"Assessing the recall rate for screening mammography: Comparing the medicare hospital compare dataset with the national mammography database","volume":"211","author":"Lee","year":"2018","journal-title":"Am. J. Roentgenol."},{"key":"ref_8","first-page":"1","article-title":"A machine-learning approach to forecast aggravation risk in patients with acute exacerbation of chronic obstructive pulmonary disease with clinical indicators","volume":"10","author":"Peng","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.future.2018.12.060","article-title":"Revisiting signal processing with spectrogram analysis on EEG, ECG and speech signals","volume":"98","author":"Wang","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18\u201322). Shufflenet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s13246-015-0333-x","article-title":"Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques","volume":"38","author":"Amin","year":"2015","journal-title":"Australas. Phys. Eng. Sci. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.cca.2021.10.005","article-title":"Detection of COPD and Lung Cancer with electronic nose using ensemble learning methods","volume":"523","author":"Binson","year":"2021","journal-title":"Clin. Chim. Acta"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Giannakopoulos, T., and Pikrakis, A. (2014). Introduction to Audio Analysis: A MATLAB\u00ae Approach, Academic Press.","DOI":"10.1016\/B978-0-08-099388-1.00001-7"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1109\/JSTSP.2019.2908700","article-title":"Deep learning for audio signal processing","volume":"13","author":"Purwins","year":"2019","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1080\/15412555.2019.1688278","article-title":"Machine learning-based prediction models for 30-day readmission after hospitalization for chronic obstructive pulmonary disease","volume":"16","author":"Goto","year":"2019","journal-title":"COPD J. Chronic Obstr. Pulm. Dis."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1109\/TIM.2016.2647458","article-title":"Short-frequency Fourier transform for fault diagnosis of induction machines working in transient regime","volume":"66","year":"2017","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.dsp.2017.11.003","article-title":"Short-time Fourier transform with the window size fixed in the frequency domain","volume":"77","author":"Mateo","year":"2018","journal-title":"Digit. Signal Process."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Dinh, P.H. (2021). Combining gabor energy with equilibrium optimizer algorithm for multi-modality medical image fusion. Biomed. Signal Process. Control, 68.","DOI":"10.1016\/j.bspc.2021.102696"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1111\/joes.12012","article-title":"The continuous wavelet transform: Moving beyond uni-and bivariate analysis","volume":"28","author":"Soares","year":"2014","journal-title":"J. Econ. Surv."},{"key":"ref_20","unstructured":"Tan, M., and Le, Q. (, January 9\u201315). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning. PMLR, Long Beach, CA, USA."},{"key":"ref_21","unstructured":"Iandola, F.N. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size. arXiv."},{"key":"ref_22","unstructured":"Howard, A.G. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Koonce, B., and Koonce, B. (2021). EfficientNet. Convolutional Neural Networks with Swift for Tensorflow: Image Recognition and Dataset Categorization, Springer.","DOI":"10.1007\/978-1-4842-6168-2"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Koonce, B., and Koonce, B. (2021). SqueezeNet. Convolutional Neural Networks with Swift for Tensorflow: Image Recognition and Dataset Categorization, Springer.","DOI":"10.1007\/978-1-4842-6168-2"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sinha, D., and El-Sharkawy, M. (2019, January 10\u201312). Thin mobilenet: An enhanced mobilenet architecture. Proceedings of the 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, USA.","DOI":"10.1109\/UEMCON47517.2019.8993089"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Petmezas, G., Cheimariotis, G.A., Stefanopoulos, L., Rocha, B., Paiva, R.P., Katsaggelos, A.K., and Maglaveras, N. (2022). Automated Lung Sound Classification Using a Hybrid CNN-LSTM Network and Focal Loss Function. Sensors, 22.","DOI":"10.3390\/s22031232"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.jbi.2018.07.014","article-title":"Relief-based feature selection: Introduction and review","volume":"85","author":"Urbanowicz","year":"2018","journal-title":"J. Biomed. Inform."},{"key":"ref_28","unstructured":"Amankwaa-Kyeremeh, B., Greet, C., Zanin, M., Skinner, W., and Asamoah, R. (2020, January 5\u20136). Selecting key predictor parameters for regression analysis using modified Neighbourhood Component Analysis (NCA) Algorithm. Proceedings of the 6th UMaT Biennial International Mining and Mineral Conference, Tarkwa, Ghana."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"31","DOI":"10.17849\/insm-47-01-31-39.1","article-title":"Random forest","volume":"47","author":"Rigatti","year":"2017","journal-title":"J. Insur. Med."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"20886","DOI":"10.1109\/JSEN.2021.3100390","article-title":"Prediction of pulmonary diseases with electronic nose using SVM and XGBoost","volume":"21","author":"Binson","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Abiyev, R.H., and Ma\u2019aitaH, M.K.S. (2018). Deep convolutional neural networks for chest diseases detection. J. Healthc. Eng., 2018.","DOI":"10.1155\/2018\/4168538"},{"key":"ref_32","first-page":"2828","article-title":"Decision tree based algorithm for intrusion detection","volume":"7","author":"Rai","year":"2016","journal-title":"Int. J. Adv. Netw. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.patrec.2017.09.036","article-title":"A novel kNN algorithm with data-driven k parameter computation","volume":"109","author":"Zhang","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"106591","DOI":"10.1016\/j.knosys.2020.106591","article-title":"Accurate detection of sleep apnea with long short-term memory network based on RR interval signals","volume":"212","author":"Faust","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"25","DOI":"10.4097\/kja.21209","article-title":"Receiver operating characteristic curve: Overview and practical use for clinicians","volume":"75","author":"Nahm","year":"2022","journal-title":"Korean J. Anesthesiol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e853","DOI":"10.1016\/S2589-7500(22)00188-1","article-title":"Interpreting area under the receiver operating characteristic curve","volume":"4","author":"Steyerberg","year":"2022","journal-title":"Lancet Digit. Health"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Xu, S., Deo, R.C., Soar, J., Barua, P.D., Faust, O., Homaira, N., Jaffe, A., Kabir, A.L., and Acharya, U.R. (2023). Automated detection of airflow obstructive diseases: A systematic review of the last decade (2013\u20132022). Comput. Methods Programs Biomed., 241.","DOI":"10.1016\/j.cmpb.2023.107746"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-019-1388-0","article-title":"Respiratory sound based classification of chronic obstructive pulmonary disease: A risk stratification approach in machine learning paradigm","volume":"43","author":"Haider","year":"2019","journal-title":"J. Med. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.compbiomed.2018.10.035","article-title":"Characterization and classification of asthmatic wheeze sounds according to severity level using spectral integrated features","volume":"104","author":"Nabi","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Infante, C., Chamberlain, D.B., Kodgule, R., and Fletcher, R.R. (2017, January 11\u201315). Classification of voluntary coughs applied to the screening of respiratory disease. Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju, Republic of Korea.","DOI":"10.1109\/EMBC.2017.8037098"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Balamurali, B., Hee, H.I., Kapoor, S., Teoh, O.H., Teng, S.S., Lee, K.P., Herremans, D., and Chen, J.M. (2021). Deep neural network-based respiratory pathology classification using cough sounds. Sensors, 21.","DOI":"10.3390\/s21165555"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Xi, J., Zhao, W., Yuan, J.E., Kim, J., Si, X., and Xu, X. (2015). Detecting lung diseases from exhaled aerosols: Non-invasive lung diagnosis using fractal analysis and SVM classification. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0139511"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3455","DOI":"10.2147\/COPD.S279850","article-title":"Prediction of obstructive lung disease from chest radiographs via deep learning trained on pulmonary function data","volume":"15","author":"Schroeder","year":"2020","journal-title":"Int. J. Chronic Obstr. Pulm. Dis."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.pdpdt.2019.10.011","article-title":"A comparative study of machine learning classifiers for risk prediction of asthma disease","volume":"28","author":"Ullah","year":"2019","journal-title":"Photodiagnosis Photodyn. Ther."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-015-0208-9","article-title":"Predicting asthma control deterioration in children","volume":"15","author":"Luo","year":"2015","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Kor, C.T., Li, Y.R., Lin, P.R., Lin, S.H., Wang, B.Y., and Lin, C.H. (2022). Explainable Machine Learning Model for Predicting First-Time Acute Exacerbation in Patients with Chronic Obstructive Pulmonary Disease. J. Pers. Med., 12.","DOI":"10.3390\/jpm12020228"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/9\/1155\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:26:00Z","timestamp":1760030760000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/9\/1155"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,1]]},"references-count":46,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["diagnostics15091155"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15091155","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,1]]}}}