Construction of an intelligent screening model for allergic rhinitis based on routine blood tests.

ABSTRACT: The incidence of allergic rhinitis (AR) has been increasing annually, severely impacting patients' quality of life and increasing socioeconomic burdens. The limitations of current diagnostic methods have made the development of efficient, low-cost early screening tools urgent. Based on routine blood test data, this study employed an ensemble hard voting strategy, a comprehensive filtering strategy, an embedding strategy, and a packing strategy to select 16 highly correlated features with a frequency of at least two occurrences as model inputs. Subsequently, the top three machine learning algorithms (K-nearest neighbor, logistic regression, random forest, decision tree, and support vector machine) were selected based on the area under the curve (AUC) metric as the base classifiers. An intel

SUBMITTER: Fan C 

PROVIDER: S-EPMC12725639 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature