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Methods: Data from 6,665 participants in the Sri Lanka Health and Ageing Study (2018-2019) are used for this research. Thirteen machine learning algorithms, including Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, Na\u00efve Bayes, K-Nearest Neighbors, Gradient Boost, XGBoost, AdaBoost, CatBoost, LightGBM, Multi-Layer Perceptron, and Probabilistic Neural Network, are employed. Results: A hybrid version of Logistic Regression and LightGBM outperformed other models, achieving an AUC of 0.9062 and 79.85% sensitivity. Key predictive features for asthma include wheezing, breathlessness with wheezing, shortness of breath attacks, coughing attacks, chest tightness, nasal allergies, physical activity, passive smoking, ethnicity, and residential sector. Conclusion: Combining Logistic Regression and LightGBM models can effectively predict adult asthma based on self-reported symptoms and demographic and behavioural characteristics. The proposed expert system assists clinicians and patients in diagnosing potential asthma cases.<\/jats:p>","DOI":"10.1177\/14604582241283968","type":"journal-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T21:55:24Z","timestamp":1726264524000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["Machine learning approaches for asthma disease prediction among adults in Sri Lanka"],"prefix":"10.1177","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5641-9332","authenticated-orcid":false,"given":"JRNA","family":"Gunawardana","sequence":"first","affiliation":[{"name":"Institute for Health Policy, Sri Lanka and Robert Gordon University, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"SD","family":"Viswakula","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Colombo, Sri Lanka"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5013-2816","authenticated-orcid":false,"given":"Ravindra P","family":"Rannan-Eliya","sequence":"additional","affiliation":[{"name":"Institute for Health Policy, Sri Lanka"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nilmini","family":"Wijemunige","sequence":"additional","affiliation":[{"name":"Institute for Health Policy, Sri Lanka"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2024,9,11]]},"reference":[{"key":"bibr1-14604582241283968","volume-title":"Asthma - what is asthma?","author":"National Heart, Lung and BI","year":"2022"},{"key":"bibr2-14604582241283968","first-page":"7","volume":"102","author":"Global Asthma Network","year":"2022","journal-title":"Int J Tubercul Lung Dis"},{"key":"bibr3-14604582241283968","author":"World Health Organization","year":"2020","journal-title":"Asthma"},{"key":"bibr4-14604582241283968","unstructured":"OECD. 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