Machine learning models for preventative mobile health asthma control.

Abstract

Introduction: Asthma attacks are set off by triggers such as pollutants from the environment, respiratory viruses, physical activity and allergens. The aim of this research is to create a machine learning model using data from mobile health technology to predict and appropriately warn a patient to avoid such triggers.

Methods: Lightweight machine learning models, XGBoost, Random Forest, and LightGBM were trained and tested on cleaned asthma data with a 70-30 train-test split. The models were measured on Precision Score, Accuracy Score, Recall Score, F1 Score and model speed.

Results: The best model, XGBoost, obtained an Accuracy score of 0.902, Recall score of 0.904, Precision score of 0.835, and F1 score of 0.860 and a model training speed of 14 s.

Conclusion: As proved by the XGBoost model, predicting asthma triggers can be a viable option for asthma control using machine learning. In addition, the actionable information of triggers, allows patients to make behavior changes. However there will still need to be work testing the system in a mobile health system.