{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T21:43:22Z","timestamp":1790286202166,"version":"4.1.0"},"reference-count":44,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,14]],"date-time":"2024-12-14T00:00:00Z","timestamp":1734134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: In the United States, chronic obstructive pulmonary disease (COPD) is a significant cause of mortality. As far as we know, it is a chronic, inflammatory lung condition that cuts off airflow to the lungs. Many symptoms have been reported for such a disease: breathing problems, coughing, wheezing, and mucus production. Patients with COPD might be at risk, since they are more susceptible to heart disease and lung cancer. Methods: This study reviews COPD diagnosis utilizing various machine learning (ML) classifiers, such as Logistic Regression (LR), Gradient Boosting Classifier (GBC), Support Vector Machine (SVM), Gaussian Na\u00efve Bayes (GNB), Random Forest Classifier (RFC), K-Nearest Neighbors Classifier (KNC), Decision Tree (DT), and Artificial Neural Network (ANN). These models were applied to a dataset comprising 1603 patients after being referred for a pulmonary function test. Results: The RFC has achieved superior accuracy, reaching up to 82.06% in training and 70.47% in testing. Furthermore, it achieved a maximum F score in training and testing with an ROC value of 0.0.82. Conclusions: The results obtained with the utilized ML models align with previous work in the field, with accuracies ranging from 67.81% to 82.06% in training and from 66.73% to 71.46% in testing.<\/jats:p>","DOI":"10.3390\/diagnostics14242822","type":"journal-article","created":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T09:17:58Z","timestamp":1734340678000},"page":"2822","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Machine Learning for Enhanced COPD Diagnosis: A Comparative Analysis of Classification Algorithms"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0142-0632","authenticated-orcid":false,"given":"Walaa H.","family":"Elashmawi","sequence":"first","affiliation":[{"name":"Department of Computer Science, Suez Canal University, Ismailia 41522, Egypt"},{"name":"Department of Computer Science, Misr International University, Cairo 11828, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adel","family":"Djellal","sequence":"additional","affiliation":[{"name":"Department of Electronics, Electrotechnics, and Automation (EEA), National Higher School of Technology and Engineering, Annaba 23000, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3727-6276","authenticated-orcid":false,"given":"Alaa","family":"Sheta","sequence":"additional","affiliation":[{"name":"Computer Science Department, Southern Connecticut State University, New Haven, CT 06515, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Salim","family":"Surani","sequence":"additional","affiliation":[{"name":"Department of Pharmacy & Medicine, Texas A&M University, College Station, TX 75428, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2615-8271","authenticated-orcid":false,"given":"Sultan","family":"Aljahdali","sequence":"additional","affiliation":[{"name":"Computer Science Department, Taif University, Taif 21944, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e127","DOI":"10.1016\/j.amepre.2021.04.011","article-title":"Chronic Obstructive Pulmonary Disease and U.S. Workers: Prevalence, Trends, and Attributable Cases Associated with Work","volume":"61","author":"Syamlal","year":"2021","journal-title":"Am. J. Prev. Med."},{"key":"ref_2","first-page":"235","article-title":"The clinical and economic burden of chronic obstructive pulmonary disease in the USA","volume":"17","author":"Guarascio","year":"2013","journal-title":"Clin. Outcomes Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e1183","DOI":"10.1016\/S2214-109X(23)00217-6","article-title":"The global economic burden of chronic obstructive pulmonary disease for 204 countries and territories in 2020\u201350: A health-augmented macroeconomic modelling study","volume":"11","author":"Chen","year":"2023","journal-title":"Lancet Glob. Health"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1378\/chest.12-1135","article-title":"Trends in the prevalence of obstructive and restrictive lung function among adults in the United States: Findings from the National Health and Nutrition Examination surveys from 1988\u20131994 to 2007\u20132010","volume":"143","author":"Ford","year":"2013","journal-title":"Chest"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1683","DOI":"10.1001\/archinte.160.11.1683","article-title":"Obstructive lung disease and low lung function in adults in the United States: Data from the National Health and Nutrition Examination Survey, 1988\u20131994","volume":"160","author":"Mannino","year":"2000","journal-title":"Arch. Intern. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1540","DOI":"10.1164\/rccm.201710-2028PP","article-title":"At the root: Defining and halting progression of early chronic obstructive pulmonary disease","volume":"197","author":"Martinez","year":"2018","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1164\/rccm.202107-1663SO","article-title":"Treatment trials in young patients with chronic obstructive pulmonary disease and pre\u2013chronic obstructive pulmonary disease patients: Time to move forward","volume":"205","author":"Martinez","year":"2022","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1513\/AnnalsATS.201308-251OC","article-title":"FEV1\/FEV6 to diagnose airflow obstruction. Comparisons with computed tomography and morbidity indices","volume":"11","author":"Bhatt","year":"2014","journal-title":"Ann. Am. Thorac. Soc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.ijtb.2020.09.002","article-title":"FEV1\/FEV6 is effective as a surrogate for FEV1\/FVC in the diagnosis of chronic obstructive pulmonary disease","volume":"68","author":"Venkatachalam","year":"2021","journal-title":"Indian J. Tuberc."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1615","DOI":"10.1590\/S0100-879X2006005000182","article-title":"Efficacy of the FEV1\/FEV6 ratio compared to the FEV1\/FVC ratio for the diagnosis of airway obstruction in subjects aged 40 years or over","volume":"40","author":"Rosa","year":"2007","journal-title":"Braz. J. Med Biol. Res."},{"key":"ref_11","unstructured":"Hashmi, M., and Cataletto, M. (2024). Asthma. StatPearls [Internet], StatPearls."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.cmpb.2017.03.023","article-title":"High-accuracy detection of airway obstruction in asthma using machine learning algorithms and forced oscillation measurements","volume":"144","author":"Amaral","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.procs.2018.10.313","article-title":"Forecasting mortality risk for patients admitted to intensive care units using machine learning","volume":"140","author":"Darabi","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.artmed.2019.01.003","article-title":"Feature-weighted survival learning machine for COPD failure prediction","volume":"96","author":"Zhang","year":"2019","journal-title":"Artif. Intell. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"952","DOI":"10.1016\/j.chest.2020.02.079","article-title":"Machine learning and prediction of all-cause mortality in COPD","volume":"158","author":"Moll","year":"2020","journal-title":"Chest"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"106528","DOI":"10.1016\/j.rmed.2021.106528","article-title":"The cardiovascular phenotype of Chronic Obstructive Pulmonary Disease (COPD): Applying machine learning to the prediction of cardiovascular comorbidities","volume":"186","author":"Nikolaou","year":"2021","journal-title":"Respir. Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e24796","DOI":"10.2196\/24796","article-title":"Machine Learning Methods for the Diagnosis of Chronic Obstructive Pulmonary Disease in Healthy Subjects: Retrospective Observational Cohort Study","volume":"9","author":"Muro","year":"2021","journal-title":"JMIR Med. Inf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107469","DOI":"10.1016\/j.envint.2022.107469","article-title":"Prediction of COPD acute exacerbation in response to air pollution using exosomal circRNA profile and Machine learning","volume":"168","author":"Meng","year":"2022","journal-title":"Environ. Int."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zafari, H., Langlois, S., Zulkernine, F., Kosowan, L., and Singer, A. (2022). AI in predicting COPD in the Canadian population. Biosystems, 211.","DOI":"10.1016\/j.biosystems.2021.104585"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, X., Ren, H., Ren, J., Song, W., Qiao, Y., Ren, Z., Zhao, Y., Linghu, L., Cui, Y., and Zhao, Z. (2023). Machine learning-enabled risk prediction of chronic obstructive pulmonary disease with unbalanced data. Comput. Methods Programs Biomed., 230.","DOI":"10.1016\/j.cmpb.2023.107340"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1016\/j.jaip.2023.01.017","article-title":"Diagnostic Performance of a Machine Learning Algorithm (Asthma\/Chronic Obstructive Pulmonary Disease [COPD] Differentiation Classification) Tool Versus Primary Care Physicians and Pulmonologists in Asthma, COPD, and Asthma\/COPD Overlap","volume":"11","author":"Kocks","year":"2023","journal-title":"J. Allergy Clin. Immunol. Pract."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e872","DOI":"10.1016\/S2589-7500(23)00177-2","article-title":"Machine learning and deep learning predictive models for long-term prognosis in patients with chronic obstructive pulmonary disease: A systematic review and meta-analysis","volume":"5","author":"Smith","year":"2023","journal-title":"Lancet Digit. Health"},{"key":"ref_23","first-page":"1849","article-title":"Artificial Intelligence in Chronic Obstructive Pulmonary Disease: Research Status, Trends, and Future Directions\u2013A Bibliometric Analysis from 2009 to 2023","volume":"19","author":"Bian","year":"2024","journal-title":"Int. J. Chronic Obstr. Pulm. Dis."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1186\/s12931-024-02960-6","article-title":"Using machine learning for early detection of chronic obstructive pulmonary disease: A narrative review","volume":"25","author":"Shen","year":"2024","journal-title":"Respir. Res."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Xu, Y., Long, Z.A., and Setyohadi, D.B. (2024, January 3\u20135). A Comprehensive Review on the Application of Artificial Intelligence in Chronic Obstructive Pulmonary Disease (COPD) Management. Proceedings of the 2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM), Kuala Lumpur, Malaysia.","DOI":"10.1109\/IMCOM60618.2024.10418416"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.2147\/COPD.S456390","article-title":"Curve-Modelling and Machine Learning for a Better COPD Diagnosis","volume":"19","author":"Cagy","year":"2024","journal-title":"Int. J. Chronic Obstr. Pulm. Dis."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2482","DOI":"10.21037\/jtd-24-416","article-title":"Developing and validating machine learning-based prediction models for frailty occurrence in those with chronic obstructive pulmonary disease","volume":"16","author":"Chen","year":"2024","journal-title":"J. Thorac. Dis."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1164\/rccm.201710-1981ST","article-title":"Recommendations for a Standardized Pulmonary Function Report. An Official American Thoracic Society Technical Statement","volume":"196","author":"Culver","year":"2017","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_29","first-page":"427","article-title":"Heart Disease Diagnosis Using Decision Trees with Feature Selection Method","volume":"21","author":"Sheta","year":"2024","journal-title":"Int. Arab. J. Inf. Technol. (IAJIT)"},{"key":"ref_30","first-page":"1372","article-title":"Utilizing Various Machine Learning Techniques for Diabetes Mellitus Feature Selection and Classification","volume":"15","author":"Sheta","year":"2024","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Sheta, A., Turabieh, H., Thaher, T., Too, J., Mafarja, M., Hossain, M.S., and Surani, S.R. (2021). Diagnosis of Obstructive Sleep Apnea from ECG Signals Using Machine Learning and Deep Learning Classifiers. Appl. Sci., 11.","DOI":"10.3390\/app11146622"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"766","DOI":"10.1007\/978-3-030-32520-6_56","article-title":"Diagnosis of obstructive sleep apnea using logistic regression and artificial neural networks models","volume":"Volume 1","author":"Sheta","year":"2020","journal-title":"Proceedings of the Future Technologies Conference (FTC) 2019"},{"key":"ref_33","unstructured":"Ranganathan, S., Gribskov, M., Nakai, K., and Sch\u00f6nbach, C. (2019). Data Mining: Prediction Methods. Encyclopedia of Bioinformatics and Computational Biology, Academic Press."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1198\/004017007000000245","article-title":"Large-Scale Bayesian Logistic Regression for Text Categorization","volume":"49","author":"Genkin","year":"2007","journal-title":"Technometrics"},{"key":"ref_35","first-page":"190","article-title":"A Review of the Logistic Regression Model with Emphasis on Medical Research","volume":"7","author":"Boateng","year":"2019","journal-title":"J. Data Anal. Inf. Process."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.neucom.2019.10.118","article-title":"A comprehensive survey on support vector machine classification: Applications, challenges and trends","volume":"408","author":"Cervantes","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, L. (2005). Support Vector Machines\u2014An Introduction. Support Vector Machines: Theory and Applications, Springer.","DOI":"10.1007\/b95439"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e01802","DOI":"10.1016\/j.heliyon.2019.e01802","article-title":"Machine learning for email spam filtering: Review, approaches and open research problems","volume":"5","author":"Dada","year":"2019","journal-title":"Heliyon"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"104767","DOI":"10.1016\/j.autcon.2023.104767","article-title":"Comparison of histogram-based gradient boosting classification machine, random Forest, and deep convolutional neural network for pavement raveling severity classification","volume":"148","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_40","first-page":"41","article-title":"An empirical study of the naive Bayes classifier","volume":"Volume 3","author":"Rish","year":"2001","journal-title":"Proceedings of the IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Sammut, C., and Webb, G.I. (2010). Decision Tree. Encyclopedia of Machine Learning, Springer.","DOI":"10.1007\/978-0-387-30164-8"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"19","DOI":"10.17010\/ijcs\/2021\/v6\/i6\/167641","article-title":"Analysis of Depth of Entropy and GINI Index Based Decision Trees for Predicting Diabetes","volume":"6","author":"Saud","year":"2022","journal-title":"Indian J. Comput. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1007\/s10044-019-00835-x","article-title":"Pedestrian detection using multiple feature channels and contour cues with census transform histogram and random forest classifier","volume":"23","author":"Braik","year":"2020","journal-title":"Pattern Anal. Appl."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/24\/2822\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:55:33Z","timestamp":1760115333000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/24\/2822"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,14]]},"references-count":44,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["diagnostics14242822"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics14242822","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,14]]}}}