Development of a Machine Learning Based Clinical Decision Support System for Classification of Migraine Types: A Preliminary Study
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DOI:
https://doi.org/10.5281/zenodo.14194306Keywords:
Neurological Headache, Migraine, Machine Learning, Classification, Clinical Decision SystemAbstract
Migraine is a type of neurological headache that seriously affects daily life and is associated
with different symptoms. Early diagnosis of migraine disease is important for the start of the treatment
process. In this process, specialized physicians are always needed, but artificial intelligence-based clinical
systems can save time in the diagnosis of migraine and other headache types and can help determine the
right treatment methods by providing support to general practitioners. In this study, the classification of
migraine typical with aura and migraine without aura, which are the most common types of migraine, and
other types of migraine were performed. In the classification process, data from demographic and clinical
questionnaires were used and five different machine learning models were applied. In this research, the
Rotation Forest algorithm showed the most successful performance according to the classifier evaluation
criteria. As a result of this algorithm, accuracy (95.14%), true positive (95.10%), false positive (2.40%),
kappa statistics (92.71%) and mean absolute error (6.50%) rates were obtained.
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