Optimized Migraine Detection in Healthcare: Exploring Random Forest and XGBoost with Prospects for Federated Learning

Migraine headaches are a widespread and complex neurovascular condition that is difficult to diagnose accurately using traditional methods. Existing diagnostic techniques are based on subjective measures of pain, which are not sufficiently reliable for an accurate diagnosis. Headaches, although common, significantly impact brain function, general health, and human well-being, making an accurate diagnosis crucial. Machine learning (ML) has become increasingly important in healthcare, offering new ways to improve diagnosis and treatment through advanced algorithms and techniques. The study uses advanced ML algorithms, random forests, and XGBoost to predict and classify different types of migraines. The models were trained on publicly available datasets, with some using data augmentation techniques such as SMOTE to enhance the training process. Some techniques like principal component analysis (PCA) were also involved to reduce the dimensionality. The models were trained to classify seven different types of migraines. The findings highlight the significant potential of artificial intelligence (AI) and ML in improving migraine diagnosis, demonstrating the transformative impact these technologies can have in healthcare, especially in resource-limited settings.