Machine Learning for Predicting the Risk for Childhood Asthma Using Prenatal, Perinatal, Postnatal and Environmental Factors

The prevalence rate for childhood asthma and its associated risk factors vary significantly across countries and regions. In the case of Morocco, the scarcity of available medical data makes scientific research on diseases such as asthma very challenging. In this paper, we build machine learning mod...

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Autores principales:	Zineb Jeddi, Ihsane Gryech, Mounir Ghogho, Maryame EL Hammoumi, Chafiq Mahraoui
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	asthma machine learning prediction risk factors environment prevention Medicine R
Acceso en línea:	https://doaj.org/article/630a899d9ccd42e89eb328b8a68945e6
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spelling	oai:doaj.org-article:630a899d9ccd42e89eb328b8a68945e62021-11-25T17:44:18ZMachine Learning for Predicting the Risk for Childhood Asthma Using Prenatal, Perinatal, Postnatal and Environmental Factors10.3390/healthcare91114642227-9032https://doaj.org/article/630a899d9ccd42e89eb328b8a68945e62021-10-01T00:00:00Zhttps://www.mdpi.com/2227-9032/9/11/1464https://doaj.org/toc/2227-9032The prevalence rate for childhood asthma and its associated risk factors vary significantly across countries and regions. In the case of Morocco, the scarcity of available medical data makes scientific research on diseases such as asthma very challenging. In this paper, we build machine learning models to predict the occurrence of childhood asthma using data from a prospective study of 202 children with and without asthma. The association between different factors and asthma diagnosis is first assessed using a Chi-squared test. Then, predictive models such as logistic regression analysis, decision trees, random forest and support vector machine are used to explore the relationship between childhood asthma and the various risk factors. First, data were pre-processed using a Chi-squared feature selection, 19 out of the 36 factors were found to be significantly associated (<i>p</i>-value < 0.05) with childhood asthma; these include: history of atopic diseases in the family, presence of mites, cold air, strong odors and mold in the child’s environment, mode of birth, breastfeeding and early life habits and exposures. For asthma prediction, random forest yielded the best predictive performance (accuracy = 84.9%), followed by logistic regression (accuracy = 82.57%), support vector machine (accuracy = 82.5%) and decision trees (accuracy = 75.19%). The decision tree model has the advantage of being easily interpreted. This study identified important maternal and prenatal risk factors for childhood asthma, the majority of which are avoidable. Appropriate steps are needed to raise awareness about the prenatal risk factors.Zineb JeddiIhsane GryechMounir GhoghoMaryame EL HammoumiChafiq MahraouiMDPI AGarticleasthmamachine learningpredictionrisk factorsenvironmentpreventionMedicineRENHealthcare, Vol 9, Iss 1464, p 1464 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	asthma machine learning prediction risk factors environment prevention Medicine R
spellingShingle	asthma machine learning prediction risk factors environment prevention Medicine R Zineb Jeddi Ihsane Gryech Mounir Ghogho Maryame EL Hammoumi Chafiq Mahraoui Machine Learning for Predicting the Risk for Childhood Asthma Using Prenatal, Perinatal, Postnatal and Environmental Factors
description	The prevalence rate for childhood asthma and its associated risk factors vary significantly across countries and regions. In the case of Morocco, the scarcity of available medical data makes scientific research on diseases such as asthma very challenging. In this paper, we build machine learning models to predict the occurrence of childhood asthma using data from a prospective study of 202 children with and without asthma. The association between different factors and asthma diagnosis is first assessed using a Chi-squared test. Then, predictive models such as logistic regression analysis, decision trees, random forest and support vector machine are used to explore the relationship between childhood asthma and the various risk factors. First, data were pre-processed using a Chi-squared feature selection, 19 out of the 36 factors were found to be significantly associated (<i>p</i>-value < 0.05) with childhood asthma; these include: history of atopic diseases in the family, presence of mites, cold air, strong odors and mold in the child’s environment, mode of birth, breastfeeding and early life habits and exposures. For asthma prediction, random forest yielded the best predictive performance (accuracy = 84.9%), followed by logistic regression (accuracy = 82.57%), support vector machine (accuracy = 82.5%) and decision trees (accuracy = 75.19%). The decision tree model has the advantage of being easily interpreted. This study identified important maternal and prenatal risk factors for childhood asthma, the majority of which are avoidable. Appropriate steps are needed to raise awareness about the prenatal risk factors.
format	article
author	Zineb Jeddi Ihsane Gryech Mounir Ghogho Maryame EL Hammoumi Chafiq Mahraoui
author_facet	Zineb Jeddi Ihsane Gryech Mounir Ghogho Maryame EL Hammoumi Chafiq Mahraoui
author_sort	Zineb Jeddi
title	Machine Learning for Predicting the Risk for Childhood Asthma Using Prenatal, Perinatal, Postnatal and Environmental Factors
title_short	Machine Learning for Predicting the Risk for Childhood Asthma Using Prenatal, Perinatal, Postnatal and Environmental Factors
title_full	Machine Learning for Predicting the Risk for Childhood Asthma Using Prenatal, Perinatal, Postnatal and Environmental Factors
title_fullStr	Machine Learning for Predicting the Risk for Childhood Asthma Using Prenatal, Perinatal, Postnatal and Environmental Factors
title_full_unstemmed	Machine Learning for Predicting the Risk for Childhood Asthma Using Prenatal, Perinatal, Postnatal and Environmental Factors
title_sort	machine learning for predicting the risk for childhood asthma using prenatal, perinatal, postnatal and environmental factors
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/630a899d9ccd42e89eb328b8a68945e6
work_keys_str_mv	AT zinebjeddi machinelearningforpredictingtheriskforchildhoodasthmausingprenatalperinatalpostnatalandenvironmentalfactors AT ihsanegryech machinelearningforpredictingtheriskforchildhoodasthmausingprenatalperinatalpostnatalandenvironmentalfactors AT mounirghogho machinelearningforpredictingtheriskforchildhoodasthmausingprenatalperinatalpostnatalandenvironmentalfactors AT maryameelhammoumi machinelearningforpredictingtheriskforchildhoodasthmausingprenatalperinatalpostnatalandenvironmentalfactors AT chafiqmahraoui machinelearningforpredictingtheriskforchildhoodasthmausingprenatalperinatalpostnatalandenvironmentalfactors
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Machine Learning for Predicting the Risk for Childhood Asthma Using Prenatal, Perinatal, Postnatal and Environmental Factors

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