{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T01:36:21Z","timestamp":1788744981418,"version":"build-2803163510"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T00:00:00Z","timestamp":1781654400000},"content-version":"vor","delay-in-days":54,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100013885","name":"Kementerian Kesihatan Malaysia","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013885","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-48762-2","type":"journal-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T04:15:56Z","timestamp":1777004156000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Prediction of the respiratory disease incidence based on environmental factors using machine learning techniques in Penang, Malaysia"],"prefix":"10.1038","volume":"16","author":[{"given":"Noraishah","family":"Mohammad Sham","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siti Syakirin","family":"Sazali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rohaida","family":"Ismail","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arief","family":"Gusnanto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmad Zia","family":"Ul-Saufie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,24]]},"reference":[{"issue":"3","key":"48762_CR1","first-page":"1","volume":"13","author":"EM Khoo","year":"2018","unstructured":"Khoo, E. M. Chronic respiratory diseases are neglected. Malays. Fam. Physician 13(3), 1 (2018).","journal-title":"Malays. Fam. Physician"},{"key":"48762_CR2","unstructured":"DOSM. Department of Statistics Malaysia. Statistics on Causes of Death, Malaysia 2024. https:\/\/www.dosm.gov.my\/site\/downloadrelease?id=statistics-on-causes-of-death-malaysia-2024 (2024)."},{"key":"48762_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2023.162130","volume":"874","author":"MI Mazeli","year":"2023","unstructured":"Mazeli, M. I., Pahrol, M. A., Shakor, A. S. A. A., Kanniah, K. D. & Omar, M. A. Cardiovascular, respiratory and all-cause (natural) health endpoint estimation using a spatial approach in Malaysia. Sci. Total Environ. 874, 162130. https:\/\/doi.org\/10.1016\/j.scitotenv.2023.162130 (2023).","journal-title":"Sci. Total Environ."},{"issue":"6","key":"48762_CR4","doi-asserted-by":"publisher","DOI":"10.3390\/atmos12060790","volume":"12","author":"E Grigorieva","year":"2021","unstructured":"Grigorieva, E. & Lukyanets, A. Combined effect of hot weather and outdoor air pollution on respiratory health: Literature review. Atmosphere 12(6), 790. https:\/\/doi.org\/10.3390\/atmos12060790 (2021).","journal-title":"Atmosphere"},{"key":"48762_CR5","doi-asserted-by":"publisher","DOI":"10.18280\/isi.260107","author":"SS Subbiah","year":"2021","unstructured":"Subbiah, S. S. & Chinnappan, J. Opportunities and challenges of feature selection methods for high dimensional data: A review. Ing. Syst. Inf. https:\/\/doi.org\/10.18280\/isi.260107 (2021).","journal-title":"Ing. Syst. Inf."},{"key":"48762_CR6","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.jclinepi.2017.12.006","volume":"98","author":"AF Schmidt","year":"2018","unstructured":"Schmidt, A. F. & Finan, C. Linear regression and the normality assumption. J. Clin. Epidemiol. 98, 146\u2013151. https:\/\/doi.org\/10.1016\/j.jclinepi.2017.12.006 (2018).","journal-title":"J. Clin. Epidemiol."},{"issue":"3","key":"48762_CR7","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1080\/20964471.2025.2547500","volume":"9","author":"M Waqas","year":"2025","unstructured":"Waqas, M. & Humphries, U. W. Artificial intelligence-driven precipitation downscaling and projections over Thailand using CMIP6 climate models. Big Earth Data 9(3), 583\u2013614. https:\/\/doi.org\/10.1080\/20964471.2025.2547500 (2025).","journal-title":"Big Earth Data"},{"key":"48762_CR8","doi-asserted-by":"publisher","DOI":"10.3390\/w16223194","volume":"16","author":"M Waqas","year":"2024","unstructured":"Waqas, M., Humphries, U. W., Hlaing, P. T. & Ahmad, S. Seasonal WaveNet-LSTM: A deep learning framework for precipitation forecasting with integrated large scale climate drivers. Water 16, 3194 (2024). https:\/\/doi.org\/10.3390\/w16223194","journal-title":"Water"},{"issue":"16","key":"48762_CR9","doi-asserted-by":"publisher","DOI":"10.3390\/w15162979","volume":"15","author":"M Waqas","year":"2023","unstructured":"Waqas, M., Humphries, U. W., Wangwongchai, A., Dechpichai, P. & Ahmad, S. Potential of artificial intelligence-based techniques for rainfall forecasting in Thailand: A comprehensive review. Water 15(16), 2979. https:\/\/doi.org\/10.3390\/w15162979 (2023).","journal-title":"Water"},{"key":"48762_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2023.119677","volume":"301","author":"JW Yong","year":"2023","unstructured":"Yong, J. W. et al. Quantification of COVID-19 impacts on NO2 and O3: Systematic model selection and hyperparameter optimization on AI-based meteorological-normalization methods. Atmos. Environ. 301, 119677. https:\/\/doi.org\/10.1016\/j.atmosenv.2023.119677 (2023).","journal-title":"Atmos. Environ."},{"key":"48762_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2021.103390","volume":"75","author":"NM Sofwan","year":"2021","unstructured":"Sofwan, N. M. et al. Risks of exposure to ambient air pollutants on the admission of respiratory and cardiovascular diseases in Kuala Lumpur. Sustain. Cities Soc. 75, 103390. https:\/\/doi.org\/10.1016\/j.scs.2021.103390 (2021).","journal-title":"Sustain. Cities Soc."},{"key":"48762_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2022.159509","volume":"858","author":"K Ravindra","year":"2023","unstructured":"Ravindra, K. et al. Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections. Sci. Total Environ. 858, 159509. https:\/\/doi.org\/10.1016\/j.scitotenv.2022.159509 (2023).","journal-title":"Sci. Total Environ."},{"issue":"38","key":"48762_CR13","doi-asserted-by":"publisher","first-page":"88431","DOI":"10.1007\/s11356-023-28682-8","volume":"30","author":"J Yang","year":"2023","unstructured":"Yang, J. et al. Application of machine learning to predict hospital visits for respiratory diseases using meteorological and air pollution factors in Linyi, China. Environ. Sci. Pollut. Res. 30(38), 88431\u201388443. https:\/\/doi.org\/10.1007\/s11356-023-28682-8 (2023).","journal-title":"Environ. Sci. Pollut. Res."},{"key":"48762_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-97-7571-2_2","author":"R Sinha","year":"2024","unstructured":"Sinha, R., Chawla, V., Palwe, S., Singh, O. & Kharmale, P. Prediction of chronic respiratory diseases using machine learning algorithms. Int. Conf. Comput. Mach. Learn. https:\/\/doi.org\/10.1007\/978-981-97-7571-2_2 (2024).","journal-title":"Int. Conf. Comput. Mach. Learn."},{"issue":"12","key":"48762_CR15","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0308015","volume":"19","author":"BP Kaur","year":"2024","unstructured":"Kaur, B. P. et al. A genetic algorithm aided hyper parameter optimization based ensemble model for respiratory disease prediction with explainable AI. PLoS ONE 19(12), e0308015. https:\/\/doi.org\/10.1371\/journal.pone.0308015 (2024).","journal-title":"PLoS ONE"},{"issue":"1","key":"48762_CR16","doi-asserted-by":"publisher","DOI":"10.1186\/s12931-024-02699-0","volume":"25","author":"H Lee","year":"2024","unstructured":"Lee, H. & Yoon, H. Y. Impact of ambient temperature on respiratory disease: A case-crossover study in Seoul. Respir. Res. 25(1), 73. https:\/\/doi.org\/10.1186\/s12931-024-02699-0 (2024).","journal-title":"Respir. Res."},{"key":"48762_CR17","doi-asserted-by":"publisher","unstructured":"Gaur, D., Mehrotra, D. & Singh, K. Estimation of particulate matter PM2.5 concentration using random forest regressor with hyperparameter tuning. In 12th Int. Conf. Cloud Comput. Data Sci. & Eng. (Confluence) 465\u2013469 https:\/\/doi.org\/10.1109\/Confluence52989.2022.9734205 (2022).","DOI":"10.1109\/Confluence52989.2022.9734205"},{"issue":"2","key":"48762_CR18","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1007\/s11277-023-10432-1","volume":"131","author":"SW Ali","year":"2023","unstructured":"Ali, S. W. et al. CDSS for early recognition of respiratory diseases based on AI techniques: A systematic review. Wirel. Pers. Commun. 131(2), 739\u2013761. https:\/\/doi.org\/10.1007\/s11277-023-10432-1 (2023).","journal-title":"Wirel. Pers. Commun."},{"key":"48762_CR19","doi-asserted-by":"publisher","DOI":"10.3389\/fped.2024.1388820","volume":"12","author":"TZ Yehuala","year":"2024","unstructured":"Yehuala, T. Z., Fente, B. M., Wubante, S. M. & Derseh, N. M. Exploring machine learning algorithms to predict acute respiratory tract infection and identify its determinants among children under five in Sub-Saharan Africa. Front. Pediatr. 12, 1388820. https:\/\/doi.org\/10.3389\/fped.2024.1388820 (2024).","journal-title":"Front. Pediatr."},{"issue":"18","key":"48762_CR20","doi-asserted-by":"publisher","first-page":"53754","DOI":"10.1007\/s11356-023-26017-1","volume":"30","author":"H Sun","year":"2023","unstructured":"Sun, H. et al. Prediction and early warning model of mixed exposure to air pollution and meteorological factors on death of respiratory diseases based on machine learning. Environ. Sci. Pollut. Res. 30(18), 53754\u201353766. https:\/\/doi.org\/10.1007\/s11356-023-26017-1 (2023).","journal-title":"Environ. Sci. Pollut. Res."},{"key":"48762_CR21","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-06277-2","author":"A Elbeltagi","year":"2025","unstructured":"Elbeltagi, A. et al. Air temperature estimation and modeling using data driven techniques based on best subset regression model in Egypt. Sci. Rep. https:\/\/doi.org\/10.1038\/s41598-025-06277-2 (2025).","journal-title":"Sci. Rep."},{"key":"48762_CR22","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-92019-3","author":"A Makhdoomi","year":"2025","unstructured":"Makhdoomi, A., Sarkhosh, M. & Ziaei, S. PM2. 5 concentration prediction using machine learning algorithms: An approach to virtual monitoring stations. Sci. Rep. https:\/\/doi.org\/10.1038\/s41598-025-92019-3 (2025).","journal-title":"Sci. Rep."},{"key":"48762_CR23","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-24045-0","author":"H Xu","year":"2025","unstructured":"Xu, H. et al. Machine learning prediction of STEMI incidence with SHAP interpretation of environmental determinants. Sci. Rep. https:\/\/doi.org\/10.1038\/s41598-025-24045-0 (2025).","journal-title":"Sci. Rep."},{"key":"48762_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104813","volume":"137","author":"K Wang","year":"2021","unstructured":"Wang, K. et al. Interpretable prediction of 3-year all-cause mortality in patients with heart failure caused by coronary heart disease based on machine learning and SHAP. Comput. Biol. Med. 137, 104813. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104813 (2021).","journal-title":"Comput. Biol. Med."},{"issue":"2","key":"48762_CR25","doi-asserted-by":"publisher","first-page":"95","DOI":"10.5539\/gjhs.v4n2p95","volume":"4","author":"LA Latiff","year":"2012","unstructured":"Latiff, L. A. et al. Pandemic influenza A (H1N1) and its prevention: A cross sectional study on patients\u2019 knowledge, attitude and practice among patients attending primary health care clinic in Kuala Lumpur, Malaysia. Glob. J. Health Sci. 4(2), 95. https:\/\/doi.org\/10.5539\/gjhs.v4n2p95 (2012).","journal-title":"Glob. J. Health Sci."},{"key":"48762_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2023.166430","volume":"901","author":"Y Zheng","year":"2023","unstructured":"Zheng, Y. et al. Assessing the impacts of climate variables on long-term air quality trends in Peninsular Malaysia. Sci. Total Environ. 901, 166430. https:\/\/doi.org\/10.1016\/j.scitotenv.2023.166430 (2023).","journal-title":"Sci. Total Environ."},{"key":"48762_CR27","unstructured":"Shahwahid, H. O. The economic value of the June 2013 haze impacts on Peninsular Malaysia. Economy and Environment Program for Southeast Asia. https:\/\/ideas.repec.org\/p\/eep\/report\/rr2016013 (2016)."},{"key":"48762_CR28","doi-asserted-by":"publisher","DOI":"10.2196\/13075","author":"J Peng","year":"2020","unstructured":"Peng, J. et al. Peak outpatient and emergency department visit forecasting for patients with chronic respiratory diseases using machine learning methods: Retrospective cohort study. JMIR Med. Inform. https:\/\/doi.org\/10.2196\/13075 (2020).","journal-title":"JMIR Med. Inform."},{"issue":"2","key":"48762_CR29","doi-asserted-by":"publisher","first-page":"168","DOI":"10.21053\/ceo.2021.01536","volume":"15","author":"Y Ku","year":"2022","unstructured":"Ku, Y., Kwon, S. B., Yoon, J. H., Mun, S. K. & Chang, M. Machine learning models for predicting the occurrence of respiratory diseases using climatic and air-pollution factors. Clin. Exp. Otorhinolaryngol. 15(2), 168\u2013176. https:\/\/doi.org\/10.21053\/ceo.2021.01536 (2022).","journal-title":"Clin. Exp. Otorhinolaryngol."},{"key":"48762_CR30","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2025.1513926","volume":"13","author":"H Su","year":"2025","unstructured":"Su, H. & Xie, H. Associations between lifestyle habits, environmental factors and respiratory diseases: A cross-sectional study from southwest China. Front. Public Health 13, 1513926. https:\/\/doi.org\/10.3389\/fpubh.2025.1513926 (2025).","journal-title":"Front. Public Health"},{"issue":"1","key":"48762_CR31","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-025-56573-8","volume":"16","author":"G Camps-Valls","year":"2025","unstructured":"Camps-Valls, G. et al. Artificial intelligence for modeling and understanding extreme weather and climate events. Nat. Commun. 16(1), 1919. https:\/\/doi.org\/10.1038\/s41467-025-56573-8 (2025).","journal-title":"Nat. Commun."},{"issue":"3","key":"48762_CR32","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1111\/1759-7714.13323","volume":"11","author":"Y Cao","year":"2020","unstructured":"Cao, Y., Chen, M., Dong, D., Xie, S. & Liu, M. Environmental pollutants damage airway epithelial cell cilia: Implications for the prevention of obstructive lung diseases. Thorac. Cancer 11(3), 505\u2013510. https:\/\/doi.org\/10.1111\/1759-7714.13323 (2020).","journal-title":"Thorac. Cancer"},{"key":"48762_CR33","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2023.1106336","volume":"11","author":"J Song","year":"2023","unstructured":"Song, J. et al. Association of ambient carbon monoxide exposure with hospitalization risk for respiratory diseases: A time series study in Ganzhou, China. Front. Public Health 11, 1106336. https:\/\/doi.org\/10.3389\/fpubh.2023.1106336 (2023).","journal-title":"Front. Public Health"},{"key":"48762_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.clim.2021.108754","volume":"227","author":"P Misiukiewicz-Stepien","year":"2021","unstructured":"Misiukiewicz-Stepien, P. & Paplinska-Goryca, M. Biological effect of PM10 on airway epithelium-focus on obstructive lung diseases. Clin. Immunol. 227, 108754. https:\/\/doi.org\/10.1016\/j.clim.2021.108754 (2021).","journal-title":"Clin. Immunol."},{"key":"48762_CR35","doi-asserted-by":"publisher","DOI":"10.4046\/trd.2020.0154","author":"SY Kim","year":"2020","unstructured":"Kim, S. Y., Kim, E. & Kim, W. J. Health effects of ozone on respiratory diseases. Tuberc. Respir. Dis. https:\/\/doi.org\/10.4046\/trd.2020.0154 (2020).","journal-title":"Tuberc. Respir. Dis."},{"key":"48762_CR36","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1007\/978-3-030-89010-0_4","volume-title":"Multivariate Statistical Machine Learning Methods for Genomic Prediction","author":"OA Montesinos L\u00f3pez","year":"2022","unstructured":"Montesinos L\u00f3pez, O. A., Montesinos L\u00f3pez, A. & Crossa, J. Overfitting, model tuning, and evaluation of prediction performance. In Multivariate Statistical Machine Learning Methods for Genomic Prediction 109\u2013139 (Springer International Publishing, 2022). https:\/\/doi.org\/10.1007\/978-3-030-89010-0_4."},{"key":"48762_CR37","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/4462018","author":"ES Lee","year":"2022","unstructured":"Lee, E. S. et al. A machine learning\u2010based study of the effects of air pollution and weather in respiratory disease patients visiting emergency departments. Emerg. Med. Int. https:\/\/doi.org\/10.1155\/2022\/4462018 (2022).","journal-title":"Emerg. Med. Int."},{"key":"48762_CR38","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2025.1537238","volume":"13","author":"X Yang","year":"2025","unstructured":"Yang, X., Li, Y., Liu, L. & Zang, Z. Prediction of respiratory diseases based on random forest model. Front. Public Health 13, 1537238. https:\/\/doi.org\/10.3389\/fpubh.2025.1537238 (2025).","journal-title":"Front. Public Health"},{"issue":"1","key":"48762_CR39","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-22972-6","volume":"15","author":"C Zhang","year":"2025","unstructured":"Zhang, C. & Liu, L. Machine learning prediction model for medical environment comfort based on SHAP and LIME interpretability analysis. Sci. Rep. 15(1), 39269. https:\/\/doi.org\/10.1038\/s41598-025-22972-6 (2025).","journal-title":"Sci. Rep."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-48762-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-48762-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-48762-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T23:59:03Z","timestamp":1781654343000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-48762-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,24]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["48762"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-48762-2","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,24]]},"assertion":[{"value":"3 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"18822"}}