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@article{207293,
        author = {Aman Singh and Gargi Kushvaha and Tanu Gangwar and Ms Kajal},
        title = {An Interpretable Machine Learning Framework for Mi-graine Prediction from Multimodal Patient Data},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {232-237},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207293},
        abstract = {Migraine is a common neurological disorder that signifi-cantly affects patients' quality of life, making early pre-diction essential for timely intervention. This study pre-sents an interpretable machine learning framework for migraine prediction using multimodal patient data, in-cluding demographic, clinical, lifestyle, environmental, and physiological features obtained from a Kaggle da-taset. The proposed framework incorporates data pre-processing, feature engineering, and comparative analy-sis of multiple machines learning algorithms, including Naïve Bayes, Random Forest, Logistic Regression, and Support Vector Machine (SVM). Experimental results demonstrate that the proposed approach effectively predicts migraine risk, with the Naïve Bayes classifier achieving the highest accuracy of 93.5%. The frame-work offers a reliable and efficient solution for early migraine prediction and has the potential to support intelligent healthcare systems and personalized patient management.},
        keywords = {Migraine Prediction, Multimodal Data, Machine Learn-ing, SVM, Random Forest, Logistic Regression, Deci-sion Tree.},
        month = {July},
        }