Clinical Prediction of Posttreatment Migraine Recurrence Using Biofeedback Data: A Machine Learning Framework for Enhanced Patient Stratification and Treatment Monitoring.

Migraine is a complex neurological disorder with significant implications for individual well-being and public health. Predicting migraine occurrences after treatment is crucial for evaluating therapeutic efficacy and enabling personalized care, yet remains largely underexplored. This study proposes a robust machine learning framework to predict posttreatment migraine headache occurrences using real-world headache log data collected from 133 patients undergoing biofeedback therapy. The methodology includes rigorous data preprocessing, outlier removal via the interquartile range (IQR) method, and class imbalance correction through the synthetic minority oversampling technique (SMOTE). A total of 10 classical and a hybrid ensemble machine learning models were developed and optimized through GridSearch with fivefold cross-validation. Performance was evaluated using different metrics, with the best-performing hybrid ensemble model achieving an accuracy and F1-score of 81%, with an area under the receiver operating characteristic curve (AUROC) of 0.87. Additionally, permutation feature importance analysis was employed to enhance model interpretability, identifying medication status, duration of treatment, and patient age as critical predictors. These outcomes validate the prospect of explainable AI-driven models in forecasting migraine recurrence posttreatment, providing a step forward toward intelligent clinical decision support systems for migraine management.