Prediction of benign paroxysmal positional vertigo recurrence in postmenopausal women: a machine learning-based clinical study - Xueqin Mi; Xuemeng Xu; Lei Fan; Qingchun Pan (Frontiers in Neurology, 2026)
Background Benign paroxysmal positional vertigo (BPPV) is a common peripheral vestibular disorder with a high recurrence rate that significantly impairs patients’ quality of life. Multiple studies have confirmed that postmenopausal women, due to endocrine disorders and other physiological characteristics, represent a high-risk population for both BPPV onset and recurrence. However, efficient recurrence prediction models are currently lacking, and traditional assessment methods have limited efficacy, making it difficult to meet the needs of individualized intervention. The aim of this study was to construct and validate machine learning models for predicting BPPV recurrence in postmenopausal women, identify key risk factors, and provide evidence for early identification of high-risk populations in clinical practice. Methods We retrospectively analyzed data from BPPV patients diagnosed and successfully treated at three hospitals in Sichuan Province between January 2023 and December 2025. Patients were divided into training and validation sets at a 7:3 ratio. Features were screened using LASSO regression and the Boruta algorithm. Six machine learning models were constructed: Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Gradient Boosting Machine (GBM), and Meta-Ensemble method. Model predictive performance was compared, and model interpretability was assessed through SHapley Additive exPlanations (SHAP) analysis. Results Through dual algorithmic screening, LASSO, and Boruta delineated four model-selected predictors for BPPV recurrence in postmenopausal women: osteoporosis, serum calcium, vitamin D, and estradiol. Among the six machine learning algorithms evaluated, the Meta-Ensemble model yielded the highest AUC point estimate of 0.857 (95% confidence interval [CI], 0.779–0.928), delivering a harmonized overall performance. Subsequent SHAP analysis elucidated that decreased serum levels of vitamin D, calcium, and estradiol, compounded by the presence of osteoporosis, were the most critical factors driving high recurrence risk predictions. Conclusion The Meta-Ensemble model can effectively predict BPPV recurrence risk in postmenopausal women. The four indicators of osteoporosis, serum calcium, vitamin D, and estradiol provide objective evidence for early clinical identification of high-risk populations for recurrence, facilitating the development of individualized follow-up strategies and intervention protocols.