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

Change Fan, Yanan Wang, Xin Tong, Shiyu Wu, Caiyan An, Huijiao Cai, Junjing Zhang, Biao Song, Ruihuan Zhang

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.

Introduction

Allergic rhinitis (AR) is a global disease caused by complex interactions between genetic and environmental factors and affects 400 million people worldwide. Environmental exposure, climate change and lifestyle are all risk factors for AR.

Materials and methods 

This study included AR patients who visited the outpatient department of the First Hospital of Hohhot from March 21, 2023 to September 3, 2023. Specifically, the data from 26 routine tests (Table 1) and the AR diagnosis results of patients aged 18–70 years were included.

Results

After multiple adjustments and optimizations, the final feature selection method and parameter settings are as follows: The filtering method (mutual information) directly calculates the correlation between feature data and labels without additional parameters, and selects the top 15 features as the screening results

Discussion

In recent years, the prevalence of AR has increased annually, and 37% of allergic people gradually develop allergic asthma within 5 years or rapidly develop allergic asthma in extreme weather conditions (such as thunderstorms). Some patients may even experience anaphylactic shock due to the consumption of plant-derived foods related to allergic pollen [41].

Acknowledgments 

This study was supported by the Inner Mongolia Intelligent Big Data Research Institute team, whose valuable advice and support greatly contributed to the experimental guidance and manuscript revisions.

Citation: Fan C, Wang Y, Tong X, Wu S, An C, Cai H, et al. (2025) Construction of an intelligent screening model for allergic rhinitis based on routine blood tests. PLoS One 20(12): e0337561. https://doi.org/10.1371/journal.pone.0337561

Editor: Colin Johnson, Oregon State University, UNITED STATES OF AMERICA

Received: May 8, 2025; Accepted: November 10, 2025; Published: December 23, 2025

Copyright: © 2025 Fan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.