Machine learning algorithms for the clinical diagnosis of acute atypical asthma exacerbation

To develop a predictive model for the clinical diagnosis of acute atypical asthma attacks using machine learning algorithms and investigate the risk factors related to the diagnosis of atypical asthma.

This study retrospectively collected data on characteristics, symptoms, general examinations, pulmonary functional tests, and FeNO results of patients in the Aerospace Center Hospital. Five machine learning algorithms (logistic regression, decision tree, random forest, support vector machine, extreme gradient boosting) were employed to select variables for predicting outpatient cases of atypical asthma exacerbation in routine practice. A predictive model for diagnosing atypical asthma exacerbation was then developed, optimized, and subjected to explanatory analysis.

After screening, 214 cases were included, with 98 diagnosed with acute exacerbation of atypical asthma and 116 undiagnosed. All patients were randomly assigned into a training set (n=149) or a validation set (n=65) at a ratio of 7:3. The predictive capabilities of five models were evaluated in the validation set. This demonstrated that all models could effectively identify patients with acute exacerbation of atypical asthma; among them, Logistic regression, random forest, and extreme gradient boosting achieved accuracies of 93.1%, and extreme gradient boosting reached 95.4%. The logistic regression model showed the best predictive performance. Model interpretation analysis revealed that FeNO, EOS, FEV1 variability, history of allergic rhinitis, and wheezing during acute attacks were significant risk factors for predicting acute exacerbations of atypical asthma.

The application of machine learning methods for variable selection in predicting acute exacerbations of atypical asthma has shown promising results. FeNO, EOS, FEV1 variability, history of allergic rhinitis, and wheezing during acute episodes were crucial predictors of exacerbations.