Abstract

Neurological diseases, such as Alzheimer's, Parkinson's, and multiple sclerosis, represent major challenges to healthcare systems due to their complex nature and late-stage diagnosis. Early detection and accurate prediction of these conditions are essential for improving patient outcomes. This paper explores the use of Artificial Intelligence (AI) and Machine Learning (ML), particularly Deep Neural Networks (DNNs), to predict and manage neurological diseases. The model integrates diverse data sources, including clinical histories, brain scans, genetic information, and the sensor data from wearable devices. This innovative approach offers the potential for more efficient diagnosis, personalized treatment, and proactive management of neurological disorders. By leveraging AI and ML, healthcare providers can improve decision-making, reduce hospital visits, and deliver more effective patient care. The integration of these models into mobile and wearable devices enables continuous monitoring, providing the insights into patient conditions. The results demonstrate strong model performance, with an accuracy of 92%, precision of 89%, recall of 87%, and an F1-score of 88%, highlighting the promise of AI in revolutionizing neurological disease management.