Development of childhood asthma prediction models using machine learning approaches

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

Research output: Contribution to journalArticlepeer-review

10 Citations (SciVal)
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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.

Original languageEnglish
Article numbere12076
Number of pages12
JournalClinical and Translational Allergy
Volume11
Issue number9
DOIs
Publication statusPublished - 07 Nov 2021

Keywords

  • asthma
  • childhood
  • machine learning
  • prediction

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