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Machine learning (ML) techniques are increasingly used for risk prediction. This study identifies risk factors for asthma attacks in New Zealand and evaluates ML models’ performance. National health datasets from 355,113 patients aged 6+ were analyzed from 2008–2016, with attack occurrence within 3 months as the outcome. XGBoost and Random Forest were compared with Logistic Regression, showing slightly stronger results for XGBoost with under-sampling.","Edinburgh Research Explorer  \nPredicting the risk of asthma attacks in New Zealand using machine learning  \nCitation for published version:  \nJayamini, WKD, Mirza, F, Asif Naeem, M, Chan, AHY, Tomlin, A, Tibble, H & Beyene, KA 2025, Predicting the risk of asthma attacks in New Zealand using machine learning. in TX Bui (ed. ), Proceedings of the 58th Hawaii International Conference on System Sciences, HICSS 2025. Proceedings of the Annual Hawaii International Conference on System Sciences, ScholarSpace / AIS Electronic Library (AISeL), pp. 3731- 3741, 58th Hawaii International Conference on System Sciences, HICSS 2025, Honolulu, United States, 7/01/25 . [https://doi.org/10.24251/HICSS.2025.448](https://doi.org/10.24251/HICSS.2025.448)  \nDigital Object Identifier (DOI):  \n10.24251/HICSS.2025.448  \nLink:  \nLink to publication record in Edinburgh Research Explorer  \nDocument Version:  \nPublisher's PDF, also known as Version of record  \nPublished In:  \nProceedings of the 58th Hawaii International Conference on System Sciences, HICSS 2025  \nGeneral rights  \nCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please [contact openaccess@ed.ac.uk](contact openaccess@ed.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 24. Nov. 2025  \nProceedings of the 58th Hawaii International Conference on System Sciences | 2025  \nPredicting the risk of asthma attacks in New Zealand using machine learning  \nWidana Kankanamge Darsha Jayamini Auckland University of Technology, NZ [darsha.jayamini@autuni.ac.nz](darsha.jayamini@autuni.ac.nz)  \nAmy Hai Yan Chan University of Auckland, NZ[a.chan@auckland.ac.nz](a.chan@auckland.ac.nz)  \nFarhaan Mirza Auckland University of Technology, NZ [farhaan.mirza@aut.ac.nz](farhaan.mirza@aut.ac.nz)  \nAndrew Tomlin University of Auckland, NZ [andrew.tomlin@auckland.ac.nz](andrew.tomlin@auckland.ac.nz)  \nM.Asif Naeem National University of Computer & Emerging Sciences (NUCES), Pakistan [asif.naeem@nu.edu.pk](asif.naeem@nu.edu.pk)  \nHolly Tibble University of Edinburgh, UK  [holly.tibble@ed.ac.uk](holly.tibble@ed.ac.uk)  \nKebede Abera Beyene  \nUniversity of Health Sciences and Pharmacy in St. Louis, USA  \n [kebede.beyene@uhsp.edu](kebede.beyene@uhsp.edu)  \nAbstract  \nExploring factors that increase the risk of asthma attacks is crucial for timely patient management. Machine learning (ML) techniques are increasingly used for risk prediction. This study aimed to identify risk factors for asthma attacks in New Zealand and evaluate ML algorithms’ performance in predicting these risks. National health datasets from 355,113 patients aged 6 years and older with asthma were analyzed from 2008 to 2016. The outcome was the occurrence of an asthma attack within 3 months. Two ML models, XGBoost and Random Forest, and a statistical model, Logistic Regression (LR), were developed and performance compared. Key risk predictors included prior asthma attacks, length of winter exposure, and the number of ICS and SABA inhalers. XGB with random under-sampling performed slightly better (AUROC=0. 76, F1 score=0.33). ML models performed slightly better than LR-RUS (AUROC=0.75, F1 score=0.32) in predicting asthma attacks. Future research should explore other ML and data imbalance handling techniques to enhance risk prediction.  \nKeywords: Asthma Attacks, Risk Prediction, Machine Learning, Data Imbalance Handling  \n1. Introduction  \nAsthma is a non-communicable disease and oneof the most common chronic respiratory dise","cbCaiuimKjm43pdm","https://ap.wps.com/l/cbCaiuimKjm43pdm","pdf",694643,1,12,"English","en",105,"# Introduction\n## Study objective and motivation\n## Background: asthma burden and triggers\n# Methods\n## Data sources and cohort\n## Outcome definition\n## Models compared","[{\"question\":\"What was the main goal of the study on asthma attacks in New Zealand?\",\"answer\":\"To identify key risk factors for asthma attacks in New Zealand and evaluate the performance of machine learning algorithms for predicting those risks.\"},{\"question\":\"What dataset was used and what time period did it cover?\",\"answer\":\"The study used national health datasets from 355,113 patients aged 6 years and older with asthma, analyzing records from 2008 to 2016.\"},{\"question\":\"How was an “asthma attack” defined in the study?\",\"answer\":\"The outcome was whether an asthma attack occurred within 3 months.\"}]","Predicting the Risk of Asthma Attacks in New Zealand using Machine Learning - Conference Paper | PDF",1785936667,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-the-risk-of-asthma-attacks-in-new-zealand-using-machine-learning-conference-paper","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-the-risk-of-asthma-attacks-in-new-zealand-using-machine-learning-conference-paper/127070/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What was the main goal of the study on asthma attacks in New Zealand?","Question",{"text":75,"@type":76},"To identify key risk factors for asthma attacks in New Zealand and evaluate the performance of machine learning algorithms for predicting those risks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset was used and what time period did it cover?",{"text":80,"@type":76},"The study used national health datasets from 355,113 patients aged 6 years and older with asthma, analyzing records from 2008 to 2016.",{"name":82,"@type":73,"acceptedAnswer":83},"How was an “asthma attack” defined in the study?",{"text":84,"@type":76},"The outcome was whether an asthma attack occurred within 3 months.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]