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Motarjem, Kiomars, Moghimbeygi, Meisam. (1403). Prediction of Anoxic Tonic Seizures due to Asthma in Children Using Machine Learning Methods. , 35(1), 63-69. doi: 10.22059/jsciences.2024.372994.1007850
Kiomars Motarjem; Meisam Moghimbeygi. "Prediction of Anoxic Tonic Seizures due to Asthma in Children Using Machine Learning Methods". , 35, 1, 1403, 63-69. doi: 10.22059/jsciences.2024.372994.1007850
Motarjem, Kiomars, Moghimbeygi, Meisam. (1403). 'Prediction of Anoxic Tonic Seizures due to Asthma in Children Using Machine Learning Methods', , 35(1), pp. 63-69. doi: 10.22059/jsciences.2024.372994.1007850
Motarjem, Kiomars, Moghimbeygi, Meisam. Prediction of Anoxic Tonic Seizures due to Asthma in Children Using Machine Learning Methods. , 1403; 35(1): 63-69. doi: 10.22059/jsciences.2024.372994.1007850

Prediction of Anoxic Tonic Seizures due to Asthma in Children Using Machine Learning Methods

Journal of Sciences, Islamic Republic of Iran
مقاله 6، دوره 35، شماره 1، خرداد 2024، صفحه 63-69    اصل مقاله (437.85 K)
نوع مقاله: Original Paper
شناسه دیجیتال (DOI): 10.22059/jsciences.2024.372994.1007850
نویسندگان
Kiomars Motarjem1؛ Meisam Moghimbeygi* 2
11 Department of Statistics, Faculty of Mathematical Sciences, Tarbiat Modares University‎, ‎Tehran‎, ‎ Islamic Republic of Iran
22 Department of Mathematics, Faculty of Mathematics and Computer Science, Kharazmi University, ‎Tehran‎, ‎ Islamic Republic of Iran
چکیده
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.
کلیدواژه‌ها
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
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