Predictive Analysis of Chronic Migraine Symptoms Detection and Management Using IoT and ML

Migraine headaches, whether chronic or episodic, are difficult to manage because of the wide variety of symptoms that may develop. This research presents a novel approach to chronic migraine symptom monitoring and treatment by combining Internet of Things (IoT) devices with Gradient Boosting, a powerful machine-learning method. Sensors connected to the IoT continuously monitor vital signs like HRV, sleep cycles, and environmental variables. By analyzing this diverse mix of data, the Gradient Boosting algorithm can accurately predict when migraines will start, how severe they will be, and what factors may trigger them. Personalized treatment recommendations based on predictive insights and real-time analytics are potential by scalable data processing supported by centralized cloud architecture. The proposed method is an attempt to improve proactive healthcare management for those suffering from chronic migraines by using advanced analytics and IoT technologies. By allowing early symptom identification and personalized therapy approaches, timely interventions guided by predictive models can potentially enhance patient outcomes. The effectiveness of Gradient Boosting in managing complicated data exchanges and improving decision-making skills in chronic illness management is shown in this study, which adds to the expanding area of healthcare solutions offered by IoT. A potential way to improve treatment results and quality of life for those with chronic migraine.