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Clinical Prediction of Posttreatment Migraine Recurrence Using Biofeedback Data: A Machine Learning Framework for
Shibbir Ahmed Arif1, Ferdib-Al-Islam2, Mehidy Hasan Sium2
1School of Computing, Montclair State University, Montclair, New Jersey, USA, montclair.edu.
Biomed Research International
|March 20, 2026
Summary
This study developed an AI model to predict post-treatment migraine headaches using patient data. The model accurately forecasts migraine recurrence, aiding personalized patient care and treatment evaluation.
Area of Science:
- Neurology
- Artificial Intelligence
- Data Science
Background:
- Migraine is a complex neurological disorder impacting public health.
- Predicting post-treatment migraine occurrences is vital for therapeutic evaluation and personalized care but is underexplored.
Purpose of the Study:
- To develop a machine learning framework for predicting posttreatment migraine headache occurrences.
- To utilize real-world headache log data from patients undergoing biofeedback therapy.
- To enhance clinical decision support for migraine management.
Main Methods:
- Data preprocessing, outlier removal (IQR), and class imbalance correction (SMOTE) were applied.
- 10 classical and hybrid ensemble machine learning models were developed and optimized using GridSearch and cross-validation.
- Permutation feature importance analysis was used for model interpretability.
Main Results:
- The best hybrid ensemble model achieved 81% accuracy and F1-score, with an AUROC of 0.87.
- Key predictors identified include medication status, treatment duration, and patient age.
- Explainable AI models show promise in forecasting migraine recurrence.
Conclusions:
- Machine learning models can effectively predict posttreatment migraine occurrences.
- Explainable AI offers valuable insights for migraine management.
- This framework supports intelligent clinical decision-making for personalized migraine care.
