Migraine Prediction Method Using Feature Selection with Hybrid Optimization Method

Migraine is a common and painful neurological condition that is often characterized by one-sided, throbbing, and pulsating headaches. Unfortunately, a lot of people who suffer from migraines get misdiagnosed when the doctors use the conventional diagnostic criteria made by the IHS (International Headache Society). As a result, there is great interest in the development of automated methods that could assist in the diagnosis of migraines. This work offers a new method for diagnosing migraines through the integration of genetic algorithms and the PSO algorithm to select features, followed by classification using Random Forest. The achieved methodology demonstrates an accuracy of 99.63% which is superior to that of the conventional methods including SVM, XGBoost, Decision Tree, Random Forest, and the Stacking algorithms. Through this technique, the identification of malicious URLs is done with great accuracy as the false alarms and missed detections are minimized. An applicable scheme is the basis for the development of more sophisticated devices for the purposes of predicting migraines. In the case of everyday life, the upcoming work is going to focus on the assessment of the scalability and efficiency.