Machine learning for screening laryngopharyngeal reflux symptoms in college students: a cross-sectional study.

ABSTRACT:

Bckground

Laryngopharyngeal reflux (LPR) is a widespread global health issue. Its recurring symptoms and impact on quality of life create significant economic burdens for individuals and society. To examine the links between lifestyle, diet, and LPR symptoms (LPRS) in college students, and to build an LPRS screening model using a Genetic Algorithm (GA)-Stacking method.

Patients and methods

A cross-sectional study of 502 undergraduates from 21 universities in Jilin Province, China, using an electronic questionnaire. LPRS were assessed via the Reflux Symptom Index (RSI). Associations were analyzed with multiple methods, and a GA-Stacking screening model was developed.

Results

LPRS prevalence was 50.20% (252/502). Significant risk factors included frequent fried food con

SUBMITTER: Li S 

PROVIDER: S-EPMC12777997 | biostudies-literature | 2026 Dec

REPOSITORIES: biostudies-literature