Building Classification Models for Early Detection of Asthma in Child for US Population

Oregon Convention Center 

Asthma is the most prominent chronic disease in children and one of the most challenging ailments to diagnose in infants and preschoolers. Utilizing the BRFSS (2011-2020) data, this study focuses on building an efficient data-driven analytical predictive model based on the 28 associated risk factors and identifying the most contributing factors influencing the childhood asthma using the XGBoost (eXtreme Gradient Boosting) algorithm.
Respondents were randomly divided into training and testing samples. The grid-search mechanism was implemented to compute the optimum values of the hyper-parameters of the analytical XGBoost model. The fitted XGBoost model was compared with four competing ML models including support vector machine (SVM), random forest, LASSO regression, and GBM. The performance of all the models was compared using accuracy, AUC, precision, and recall.
XGBoost was found to be the best performing model with AUC 0.96, followed by SVM (AUC 0.93).
The analytical methodology of the model development can be instrumental in predicting different types of chronic lung diseases affecting people of all ages from multidimensional behavioral health survey data.

Childhood Asthma

Predictive Modeling

BRFSS Data

XGBoost 

Section on Statistics in Epidemiology