ENHANCING MIGRAINE DIAGNOSIS: A SYMPTOM-BASED CLASSIFICATION APPROACH WITH SVM AND DECISION TREE
Migraine, a widespread neurological condition with various symptoms, poses a significant challenge in achieving a precise diagnosis. The accurate identification of migraine can be a difficult task, considering the interaction of symptoms among different individuals. To address this diagnostic challenge, our research introduces an innovative Symptom-Based Classification Approach, utilizing advanced machine learning methods, particularly Support Vector Machines (SVM) and Decision Trees. These models are used to decipher the complexities within the wide range of migraine symptoms since they are skilled at identifying sophisticated patterns, combining the complexity of human physiology with the power of technology. To facilitate improved migraine diagnosis, we have carefully selected a large dataset that includes 400 patients' complete symptom histories, ranging in age from 15 to 77. This comprehensive collection documents the complex experiences that every person has during migraine attacks, providing the foundation for a strong and customized categorization model. This study compares SVM and Decision Trees to determine how each one contributes to the field of migraine diagnosis. The outcomes demonstrate the effectiveness of SVM over Deceions Trees and offer insightful information for future developments in accurate and customized migraine diagnosis.
Migraine, Symptom-Based Classification, Support Vector Machines, Decision Tree, Machine Learning, Healthcare, Precision Diagnosis.