Detection of Neurological Disorders Using Advanced AI Techniques

Neurological diseases have a huge impact on global health and are known to be major causes of death and disability around the world. There are more than 600 neurological diseases, but there isn't yet a single automatic standard detection method that can find more than one of them using a single framework. A major challenge with neurological disorders is changes in cognitive functions and behaviors. Diseases like Alzheimer's, Parkinson's, and schizophrenia need early detection to enable possible intervention. In this work, we emphasize non-invasive techniques for disease detection through Electroencephalography (EEG) signals using Machine Learning techniques. We, also Homogenize the three datasets under consideration and attempt multiclass classification for the three neurological disorders. We have compared the performance of proposed models with existing models for the accurate identification of neurological disorders, focusing on their efficacy and reliability. This means that EEGbased diagnostics could become one of the most prospective ways for early and accurate disease detection by applying advanced AI classification algorithms. This work contributes to ongoing efforts in neurology to improve health outcomes through artificial intelligence.