Prediction of Anoxic Tonic Seizures due to Asthma in Children Using Machine Learning Methods
The 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 o...
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University of Tehran
2024-11-01
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| Series: | Journal of Sciences, Islamic Republic of Iran |
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| Online Access: | https://jsciences.ut.ac.ir/article_97972_bdd38edf5e342e57f422538a35a34eb6.pdf |
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| author | Kiomars Motarjem Meisam Moghimbeygi |
| author_facet | Kiomars Motarjem Meisam Moghimbeygi |
| author_sort | Kiomars Motarjem |
| collection | DOAJ |
| description | The 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. |
| format | Article |
| id | doaj-art-25ed7baa593c45268bf62793c2da372a |
| institution | DOAJ |
| issn | 1016-1104 2345-6914 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | Journal of Sciences, Islamic Republic of Iran |
| spelling | doaj-art-25ed7baa593c45268bf62793c2da372a2025-08-20T03:08:50ZengUniversity of TehranJournal of Sciences, Islamic Republic of Iran1016-11042345-69142024-11-01351636910.22059/jsciences.2024.372994.100785097972Prediction of Anoxic Tonic Seizures due to Asthma in Children Using Machine Learning MethodsKiomars Motarjem0Meisam Moghimbeygi11 Department of Statistics, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, Islamic Republic of Iran2 Department of Mathematics, Faculty of Mathematics and Computer Science, Kharazmi University, Tehran, Islamic Republic of IranThe 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.https://jsciences.ut.ac.ir/article_97972_bdd38edf5e342e57f422538a35a34eb6.pdfthe 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 medicalthis study applied ml approaches to predict asthma and second anoxic tonic seizures due to asthma (atsa) based on variables such as first atsaageregion of residenceparent smoking statusand parents' asthma history. the results revealed that children's age and place of residence significantly affected the duration of asthma attackswith 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 agegenderliving regionparents' smoking statusand asthma historywith the adaboost method highlighting the importance of the child's age and living area in predicting atsa |
| spellingShingle | Kiomars Motarjem Meisam Moghimbeygi Prediction of Anoxic Tonic Seizures due to Asthma in Children Using Machine Learning Methods Journal of Sciences, Islamic Republic of Iran the 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 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 |
| title | Prediction of Anoxic Tonic Seizures due to Asthma in Children Using Machine Learning Methods |
| title_full | Prediction of Anoxic Tonic Seizures due to Asthma in Children Using Machine Learning Methods |
| title_fullStr | Prediction of Anoxic Tonic Seizures due to Asthma in Children Using Machine Learning Methods |
| title_full_unstemmed | Prediction of Anoxic Tonic Seizures due to Asthma in Children Using Machine Learning Methods |
| title_short | Prediction of Anoxic Tonic Seizures due to Asthma in Children Using Machine Learning Methods |
| title_sort | prediction of anoxic tonic seizures due to asthma in children using machine learning methods |
| topic | the 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 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 |
| url | https://jsciences.ut.ac.ir/article_97972_bdd38edf5e342e57f422538a35a34eb6.pdf |
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