Bayesian additive regression trees for predicting childhood asthma in the CHILD cohort study
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Published on: July 3, 2020
Mojtaba Ahmadiankalati1, Himani Boury1, Padmaja Subbarao2,3
1Department of Public Health Sciences, Queen's University, Kingston, ON, K7L 3N6, Canada.
Insights
Bayesian Additive Regression Trees (BART) show promise in predicting childhood asthma. This machine learning approach identified key predictors like recurrent wheeze and respiratory infections, outperforming other algorithms.
Area of Science:
- Pediatric respiratory health
- Computational epidemiology
- Machine learning in medicine
Background:
- Asthma diagnosis in children lacks a universal gold standard, relying on age-dependent clinical assessments.
- Machine learning offers potential for improved asthma diagnosis and classification.
- Limited research exists on Bayesian machine learning for childhood asthma prediction.
Purpose of the Study:
- To develop and evaluate a prediction model for childhood asthma using Bayesian Additive Regression Trees (BART).
- To compare BART's performance against other machine learning algorithms for asthma diagnosis.
- To identify key predictors and interaction effects for childhood asthma using BART.
Main Methods:
- Utilized data from 2794 participants in the CHILD Cohort Study, focusing on variables available by age 3 to predict asthma at age 5.
- Trained BART and six other machine learning algorithms (adaptive boosting, logistic regression, decision tree, neural network, random forest, support vector machine).
- Assessed model performance using sensitivity, specificity, and area under the ROC curve, with confidence intervals from bootstrapping.
Main Results:
- BART, logistic regression, and random forest demonstrated the highest predictive accuracy (area under the ROC curve).
- Key predictors identified by BART included recurrent wheeze, respiratory infection, and food sensitization at age 3.
- Significant interaction effects involved combinations of respiratory infection, wheezing, parental asthma, and child sensitization.
Conclusions:
- BART shows strong potential for predicting childhood asthma compared to other machine learning methods.
- The model identified crucial early-life risk factors and their interactions.
- External validation of BART is recommended to confirm its reliability and generalizability in diverse populations.
Background:
Asthma is a heterogeneous disease that affects millions of children and adults. There is a lack of objective gold standard diagnosis that spans the ages; instead, diagnoses are made by clinician assessment based on a cluster of signs, symptoms and objective tests dependent on age. Yet, there is a clear morbidity associated with chronic asthma symptoms. Machine learning has become a popular tool to improve asthma diagnosis and classification. There is a paucity of literature on the use of Bayesian machine learning algorithms to predict asthma diagnosis in children. This paper develops a prediction model using the Bayesian additive regression trees (BART) and compares its performance to various machine learning algorithms in predicting the diagnosis of childhood asthma.
Methods:
Clinically relevant variables collected at or before 3 years of age from 2794 participants in the CHILD Cohort Study were used to predict physician-diagnosed asthma at age 5. BART and six other commonly used machine learning algorithms, namely adaptive boosting, logistic regression, decision tree, neural network, random forest, and support vector machine were trained. Measures of performance including sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve were calculated. The confidence intervals were calculated using Bootstrapping samples. Important predictors and interaction effects associated with asthma were also identified using BART.
Results:
BART, logistic regression and random forest showed the highest area under the ROC curve compared to other machine learning algorithms. Based on BART, recurrent wheeze, respiratory infection and food sensitization at 3 years of age were the most important predictors. The three most important interaction effects were found to be interaction terms of respiratory infection at 3 years and recurrent wheezing at 3 years, maternal asthma and paternal asthma, and maternal wheezing and inhalant sensitization of child at 3 years.
Conclusions:
BART demonstrated promising prediction performance when compared to other machine learning algorithms. Future research could validate the BART in an external cohort to evaluate its reliability and generalizability.
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