Development of childhood asthma prediction models using machine learning approaches

Abstract Background Respiratory symptoms are common in early life and often transient. It is difficult to identify in which children these will persist and result in asthma. Machine learning (ML) approaches have the potential for better predictive performance and generalisability over existing child...

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Autores principales:	Dilini M. Kothalawala, Clare S. Murray, Angela Simpson, Adnan Custovic, William J. Tapper, S. Hasan Arshad, John W. Holloway, Faisal I. Rezwan, STELAR/UNICORN investigators
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Asthma Kindheit maschinelles Lernen Prognose Immunologic diseases. Allergy RC581-607
Acceso en línea:	https://doaj.org/article/eaa4da07fb3e4441aacd4bf9bf76eee0
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spelling	oai:doaj.org-article:eaa4da07fb3e4441aacd4bf9bf76eee02021-11-29T08:10:38ZDevelopment of childhood asthma prediction models using machine learning approaches2045-702210.1002/clt2.12076https://doaj.org/article/eaa4da07fb3e4441aacd4bf9bf76eee02021-11-01T00:00:00Zhttps://doi.org/10.1002/clt2.12076https://doaj.org/toc/2045-7022Abstract Background Respiratory symptoms are common in early life and often transient. It is difficult to identify in which children these will persist and result in asthma. Machine learning (ML) approaches have the potential for better predictive performance and generalisability over existing childhood asthma prediction models. This study applied ML approaches to predict school‐age asthma (age 10) in early life (Childhood Asthma Prediction in Early life, CAPE model) and at preschool age (Childhood Asthma Prediction at Preschool age, CAPP model). Methods Clinical and environmental exposure data was collected from children enrolled in the Isle of Wight Birth Cohort (N = 1368, ∼15% asthma prevalence). Recursive Feature Elimination (RFE) identified an optimal subset of features predictive of school‐age asthma for each model. Seven state‐of‐the‐art ML classification algorithms were used to develop prognostic models. Training was performed by applying fivefold cross‐validation, imputation, and resampling. Predictive performance was evaluated on the test set. Models were further externally validated in the Manchester Asthma and Allergy Study (MAAS) cohort. Results RFE identified eight and twelve predictors for the CAPE and CAPP models, respectively. Support Vector Machine (SVM) algorithms provided the best performance for both the CAPE (area under the receiver operating characteristic curve, AUC = 0.71) and CAPP (AUC = 0.82) models. Both models demonstrated good generalisability in MAAS (CAPE 8‐year = 0.71, 11‐year = 0.71, CAPP 8‐year = 0.83, 11‐year = 0.79) and excellent sensitivity to predict a subgroup of persistent wheezers. Conclusion Using ML approaches improved upon the predictive performance of existing regression‐based models, with good generalisability and ability to rule in asthma and predict persistent wheeze.Dilini M. KothalawalaClare S. MurrayAngela SimpsonAdnan CustovicWilliam J. TapperS. Hasan ArshadJohn W. HollowayFaisal I. RezwanSTELAR/UNICORN investigatorsWileyarticleAsthmaKindheitmaschinelles LernenPrognoseImmunologic diseases. AllergyRC581-607ENClinical and Translational Allergy, Vol 11, Iss 9, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Asthma Kindheit maschinelles Lernen Prognose Immunologic diseases. Allergy RC581-607
spellingShingle	Asthma Kindheit maschinelles Lernen Prognose Immunologic diseases. Allergy RC581-607 Dilini M. Kothalawala Clare S. Murray Angela Simpson Adnan Custovic William J. Tapper S. Hasan Arshad John W. Holloway Faisal I. Rezwan STELAR/UNICORN investigators Development of childhood asthma prediction models using machine learning approaches
description	Abstract Background Respiratory symptoms are common in early life and often transient. It is difficult to identify in which children these will persist and result in asthma. Machine learning (ML) approaches have the potential for better predictive performance and generalisability over existing childhood asthma prediction models. This study applied ML approaches to predict school‐age asthma (age 10) in early life (Childhood Asthma Prediction in Early life, CAPE model) and at preschool age (Childhood Asthma Prediction at Preschool age, CAPP model). Methods Clinical and environmental exposure data was collected from children enrolled in the Isle of Wight Birth Cohort (N = 1368, ∼15% asthma prevalence). Recursive Feature Elimination (RFE) identified an optimal subset of features predictive of school‐age asthma for each model. Seven state‐of‐the‐art ML classification algorithms were used to develop prognostic models. Training was performed by applying fivefold cross‐validation, imputation, and resampling. Predictive performance was evaluated on the test set. Models were further externally validated in the Manchester Asthma and Allergy Study (MAAS) cohort. Results RFE identified eight and twelve predictors for the CAPE and CAPP models, respectively. Support Vector Machine (SVM) algorithms provided the best performance for both the CAPE (area under the receiver operating characteristic curve, AUC = 0.71) and CAPP (AUC = 0.82) models. Both models demonstrated good generalisability in MAAS (CAPE 8‐year = 0.71, 11‐year = 0.71, CAPP 8‐year = 0.83, 11‐year = 0.79) and excellent sensitivity to predict a subgroup of persistent wheezers. Conclusion Using ML approaches improved upon the predictive performance of existing regression‐based models, with good generalisability and ability to rule in asthma and predict persistent wheeze.
format	article
author	Dilini M. Kothalawala Clare S. Murray Angela Simpson Adnan Custovic William J. Tapper S. Hasan Arshad John W. Holloway Faisal I. Rezwan STELAR/UNICORN investigators
author_facet	Dilini M. Kothalawala Clare S. Murray Angela Simpson Adnan Custovic William J. Tapper S. Hasan Arshad John W. Holloway Faisal I. Rezwan STELAR/UNICORN investigators
author_sort	Dilini M. Kothalawala
title	Development of childhood asthma prediction models using machine learning approaches
title_short	Development of childhood asthma prediction models using machine learning approaches
title_full	Development of childhood asthma prediction models using machine learning approaches
title_fullStr	Development of childhood asthma prediction models using machine learning approaches
title_full_unstemmed	Development of childhood asthma prediction models using machine learning approaches
title_sort	development of childhood asthma prediction models using machine learning approaches
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/eaa4da07fb3e4441aacd4bf9bf76eee0
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Development of childhood asthma prediction models using machine learning approaches

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