[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121351-en":3,"doc-seo-121351-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},121351,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",7,"Healthcare","Prediction of Anoxic Tonic Seizures due to Asthma in Children - Using Machine Learning Methods","This study examines factors shaping asthma attacks in children under six using machine learning methods. Asthma and subsequent anoxic tonic seizures due to asthma (ATSA) are predicted from variables including prior ATSA, age, residence region, parent smoking status, and parents’ asthma history. Findings show that age and residence significantly influence asthma attack duration, with some Tehran areas associated with shorter intervals, consistent with higher air pollution. AdaBoost highlights the child’s age and living area as key predictors of ATSA.","Prediction of Anoxic Tonic Seizures due to Asthma in  \nChildren  \nUsing Machine Learning Methods  \nK. Motarjem 1 and M. Moghimbeygi2*  \n1 Department of Statistics, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran,  \nIslamic Republic of Iran  \n2 Department of Mathematics, Faculty of Mathematics and Computer Science, Kharazmi University,  \nTehran, Islamic Republic of Iran  \nReceived: 22 February 2024 / Revised: 26 june 2024 / Accepted: 27 July 2024  \nAbstract  \nThe objective of this study is to investigate the factors influencing asthma attacks in children under six years old using machine learning (ML) methods. There are many statistical methods for data classification that can be used to classify medical data. But using the data itself as well as a set of different methods in machine learning can provide vast and more comparable results. Hence, this study applied ML approaches to predict asthma and second anoxic tonic seizures due to asthma (ATSA) based on variables such as first ATSA, age, region of residence, parent smoking status, and parents' asthma history. The results revealed that children's age and place of residence significantly affected the duration of asthma attacks, with children living in certain areas of Tehran experiencing shorter intervals between attacks due to high air pollution. Machine learning techniques proved useful in predicting ATSA based on age, gender, living region, parents'smoking status, and asthma history, with the AdaBoost method highlighting the importance of the child's age and living area in predicting ATSA.  \nKeywords: Asthma; Childhood; Prediction Model; Machine Learning.  \nIntroduction  \nAsthma is a chronic lung disease caused by inflammation in the airways (1) . It is the most common chronic disease, affecting 7.1 million (9.6%) of American children. Statistics show that in the United States alone in 2008, children with asthma accounted for 9.3 billion, or 8% of total direct health care costs (2) . Symptoms begin in about 80% of children with asthma before the age of six. However, only about 1/3 of children who have at least one episode of asthma symptoms by age three (3)  \ndevelop asthma at age six or more (4), that is, about 97% of children who have asthma under the age of 3 will not have asthma at the age of six years (5) .  \nPresenting and developing a model to predict whether a child will develop asthma in the future is one of the most critical issues and interests of researchers in children's asthma studies. Such a model can offer several advantages. The most important point is that timely diagnosis and treatment of asthma can prevent serious complications of asthma (6) . These allow children to enjoy long-term benefits such as fewer respiratory  \n* Corresponding Author: Tel: +989126659927; [Email: m.moghimbeygi@yahoo.com](Email: m.moghimbeygi@yahoo.com)  \nsymptoms and reduced doses of asthma control drugs (7), even if the treatment is not complete, and as a result, they have fewer drug side effects. Another advantage of obtaining a model for children's asthma is that the diagnosis of this disease is subjective for children under five years of age by doctors (8), and there is no definitive test or genetic test to definitively diagnose it in children. Developing a model can be of great help in this regard. Finally, receiving the model can directly affect the quality and lifestyle of children, because by knowing the severity of the disease, appropriate recommendations can be made to children and their parents or caregivers, from nutrition to exposure to pollution. Identifying and classifying children who are potentially more exposed to asthma can also be one of the advantages of children's asthma prediction modeling. Different approaches maybe considered for this modeling, one of the most important of which is building a model based on machine learning methods.  \nAmong the modern methods of statistical analysis, the use of machine learning (ML) has been increasing","cbCaiimr9lTVVqSy","https://ap.wps.com/l/cbCaiimr9lTVVqSy","pdf",448360,1,"English","en",105,"# Abstract\n# Introduction\n## Background on childhood asthma\n## Importance of predictive modeling\n## Machine learning methods for healthcare data\n### Unsupervised learning\n### Supervised learning","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To identify and model factors that influence asthma attacks in children under six and to predict asthma-related anoxic tonic seizures (ATSA) using machine learning.\"},{\"question\":\"Which variables are used to predict ATSA?\",\"answer\":\"The model uses first ATSA, child age, residence region, parent smoking status, and parents’ history of asthma.\"},{\"question\":\"What did the results show about age and residence?\",\"answer\":\"Children’s age and place of residence significantly affected asthma attack duration, and certain Tehran areas showed shorter intervals between attacks, linked to higher air pollution.\"}]","Prediction of Anoxic Tonic Seizures due to Asthma in Children - Using Machine Learning Methods | PDF",1785735200,18,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"prediction-of-anoxic-tonic-seizures-due-to-asthma-in-children-using-machine-learning-methods","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/healthcare/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/prediction-of-anoxic-tonic-seizures-due-to-asthma-in-children-using-machine-learning-methods/121351/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What is the main goal of the study?","Question",{"text":74,"@type":75},"To identify and model factors that influence asthma attacks in children under six and to predict asthma-related anoxic tonic seizures (ATSA) using machine learning.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which variables are used to predict ATSA?",{"text":79,"@type":75},"The model uses first ATSA, child age, residence region, parent smoking status, and parents’ history of asthma.",{"name":81,"@type":72,"acceptedAnswer":82},"What did the results show about age and residence?",{"text":83,"@type":75},"Children’s age and place of residence significantly affected asthma attack duration, and certain Tehran areas showed shorter intervals between attacks, linked to higher air pollution.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,117,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":115,"slug":116},40,"healthcare",{"id":118,"doc_module":4,"doc_module_name":45,"category_name":119,"show_sort_weight":120,"slug":121},8,"Research & Report",30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]