Development and Interpretation of a Machine Learning-Based Predictive Model Using Clinical Parameters for
Longyan Liu1, Shufen Pei1, Zengping Liu1
1Department of Otolaryngology-Head and Neck Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, China.
American Journal of Rhinology & Allergy
|March 18, 2026
Summary
Machine learning accurately predicts eosinophilic chronic rhinosinusitis with nasal polyps (ECRSwNP) using preoperative data. The XGBoost model identifies key clinical factors, aiding in noninvasive patient assessment and personalized treatment strategies.
Area of Science:
- Otolaryngology
- Medical Informatics
- Computational Biology
Background:
- Chronic rhinosinusitis with nasal polyps (CRSwNP) presents diagnostic challenges.
- Differentiating eosinophilic CRSwNP (ECRSwNP) preoperatively is crucial for effective treatment.
- Current noninvasive methods for predicting ECRSwNP subtypes have limitations.
Purpose of the Study:
- To develop and validate a machine learning model for predicting ECRSwNP using preoperative clinical data.
- To identify key clinical predictors of ECRSwNP.
- To evaluate the performance of different machine learning algorithms for this prediction task.
Main Methods:
- Retrospective analysis of preoperative clinical data from 331 CRSwNP patients.
- Utilized least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression to identify predictors.
- Developed and cross-validated four machine learning models, including XGBoost, with SHAP for interpretation.
Main Results:
- Peripheral eosinophil percentage, visual analog scale, ethmoid/maxillary sinuses ratio, and nasal polyps score were identified as significant predictors.
- The XGBoost model achieved high predictive accuracy (AUC 0.981 training, 0.928 testing).
- The model effectively predicted postoperative recurrence and demonstrated clinical utility across risk thresholds.
Conclusions:
- An interpretable XGBoost model can accurately predict ECRSwNP preoperatively.
- This model provides a valuable tool for noninvasive patient assessment and guiding individualized treatment plans.
- SHAP analysis enhances understanding of the model's predictions and identified key clinical drivers.
