Migraines are a prevalent and debilitating neurological disorder, affecting millions worldwide. Characterized by symptoms such as nausea, photophobia, phonophobia, and visual disturbances, diagnosing and classifying migraines remains a challenge due to their heterogeneous nature. This study leverages machine learning techniques to analyze a dataset comprising 400 patient records, identifying key factors that contribute to migraine classification. Using statistical analysis, correlation matrices, and Random Forest classification, we assess the significance of various symptoms in distinguishing different migraine types. Our results highlight that symptoms such as photophobia, nausea, and attack frequency play a crucial role in migraine identification. The correlation analysis reveals strong associations among specific symptoms, indicating potential patterns that can aid in classification. Furthermore, feature importance analysis using machine learning emphasizes that intensity and sensory disturbances significantly impact the accuracy of migraine type prediction. The study demonstrates the effectiveness of AI-driven methods in improving migraine classification accuracy, offering a valuable tool for clinicians to enhance diagnostic precision. The integration of machine learning into healthcare could lead to personalized treatment approaches, reducing misdiagnosis and optimizing patient management. Future research should focus on expanding datasets and incorporating deep learning models for further refinement. By harnessing AI’s predictive capabilities, this study underscores the potential for technology to revolutionize migraine diagnosis and treatment, contributing to a more efficient and patient-centered healthcare system.
Cite this paper
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