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

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 consumption (OR: 1.89; 95% CI, 1.35-2.64), late-evening meals (OR: 2.15; 95% CI, 1.54-3.01), and low physical activity (OR: 1.72; 95% CI, 1.23-2.41). The GA-Stacking model performed well, with a recall of 0.909, accuracy of 0.927, and AUC of 0.96 (95% CI, 0.94-0.98). CONCLUSIONS: Modifiable factors like fried food intake and meal timing are strongly linked to LPRS in students. The GA-Stacking model effectively identifies high-risk individuals for early intervention, highlighting the role of lifestyle changes and informing targeted health strategies.