Machine Learning Models for Early Prediction of Asthma Attacks Based on Bio-signals and Environmental Triggers

Asthma is a common respiratory disease affected by different biosignals and environmental triggers. Early prediction of asthma attacks is crucial to saving patient lives. Several machine learning models have been designed to predict asthma attacks. However, few researchers have exploited biosignals and environmental triggers to build asthma attack prediction models. Additionally, little attention has been devoted to feature selection algorithms and the variation of machine learning models. This study develops an asthma attack prediction model by testing different machine learning classifiers. The dataset used includes two main parts: the biosignals dataset, which was recorded daily from 21 volunteers for three months, and the environmental dataset, which is available online. The machine learning classifiers used in the study are decision tree, gradient boosting models, logistic regression, random forest, and support-vector machine. Each classifier was grid searched to find the best value for the primary hyperparameters; then, we used five-fold cross-validation to train each model. Results show that the gradient boost model outperforms the other classifiers when trained with 0.5 for the depth parameter and 9 for the sub-sample parameter. The prediction of the testing set produces 97.2% accuracy and 97.1 % recall.