Advanced Seizure Prediction System for Wearable Health Management Devices using KNN Algorithm

The K-Nearest Neighbors (KNN) algorithm is used to provide a novel approach to predicting seizures in wearable health monitoring devices. Physiological signals, such as EEG and ECG, are used in the preprocessing and analysis of real-time data performed on a Raspberry Pi platform by the system. After being trained on a large dataset that comprised normal and seizure states, the KNN model demonstrates good classification accuracy. The wearable equipment, intended to be unnoticeable, performs continuous analysis of physiological data and triggers alerts upon recognizing patterns that point to the possibility of an oncoming seizure. This cutting-edge gadget has the ability to identify seizures in their earliest stages, allowing for timely medical attention and intervention to be provided to patients. It is easily incorporated into one's routine and activities. The research highlights the importance of machine learning in healthcare wearables and the efficacy of the KNN algorithm for predictive analytics in neurological illnesses. It is also shown in the research that machine learning is relevant to healthcare wearables. Because of the study's emphasis on neurological disorders, this result is significant.