Development and validation of a machine learning risk prediction model for asthma attacks in adults in primary care.

ABSTRACT: Primary care consultations provide an opportunity for patients and clinicians to assess asthma attack risk. Using a data-driven risk prediction tool with routinely collected health records may be an efficient way to aid promotion of effective self-management, and support clinical decision making. Longitudinal Scottish primary care data for 21,250 asthma patients were used to predict the risk of asthma attacks in the following year. A selection of machine learning algorithms (i.e., Naïve Bayes Classifier, Logistic Regression, Random Forests, and Extreme Gradient Boosting), hyperparameters, training data enrichment methods were explored, and validated in a random unseen data partition. Our final Logistic Regression model achieved the best performance when no training data enrichment was appl

SUBMITTER: Tibble H 

PROVIDER: S-EPMC12019439 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature