Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
Exploring factors that increase the risk of asthma attacks is crucial for timely patient management. Machine learning (ML) techniques are increasingly used for risk prediction. This study aimed to identify risk factors for asthma attacks in New Zealand and evaluate ML algorithms' performance in predicting these risks. National health datasets from 355,113 patients aged 6 years and older with asthma were analyzed from 2008 to 2016. The outcome was the occurrence of an asthma attack within 3 months. Two ML models, XGBoost and Random Forest, and a statistical model, Logistic Regression (LR), were developed and performance compared. Key risk predictors included prior asthma attacks, length of winter exposure, and the number of ICS and SABA inhalers. XGB with random under-sampling performed slightly better (AUROC=0.76, F1 score=0.33). ML models performed slightly better than LR-RUS (AUROC=0.75, F1 score=0.32) in predicting asthma attacks. Future research should explore other ML and data imbalance handling techniques to enhance risk prediction.
Recommended Citation
Jayamini, Widana Kankanamge; Mirza, Farhaan; Naeem, Asif; Tomlin, Andrew; Tibble, Holly; Beyene, Kebede; and Chan, Amy, "Predicting the Risk of Asthma Attacks in New Zealand Using Machine Learning" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 3.
https://aisel.aisnet.org/hicss-58/hc/emergency_care/3
Predicting the Risk of Asthma Attacks in New Zealand Using Machine Learning
Hilton Waikoloa Village, Hawaii
Exploring factors that increase the risk of asthma attacks is crucial for timely patient management. Machine learning (ML) techniques are increasingly used for risk prediction. This study aimed to identify risk factors for asthma attacks in New Zealand and evaluate ML algorithms' performance in predicting these risks. National health datasets from 355,113 patients aged 6 years and older with asthma were analyzed from 2008 to 2016. The outcome was the occurrence of an asthma attack within 3 months. Two ML models, XGBoost and Random Forest, and a statistical model, Logistic Regression (LR), were developed and performance compared. Key risk predictors included prior asthma attacks, length of winter exposure, and the number of ICS and SABA inhalers. XGB with random under-sampling performed slightly better (AUROC=0.76, F1 score=0.33). ML models performed slightly better than LR-RUS (AUROC=0.75, F1 score=0.32) in predicting asthma attacks. Future research should explore other ML and data imbalance handling techniques to enhance risk prediction.
https://aisel.aisnet.org/hicss-58/hc/emergency_care/3