Intelligent Health Assessment Based on Communication and AI Technologies for Public Health

Abstract

Automating EEG pattern categorization in public health helps in the early detection of epilepsy and other brain illnesses. However, present methods have difficulties in reaching excellent accuracy and adaptability. This study utilizes a hybrid strategy that incorporates Spider wasp-tuned Convolutional neural network (SW-CNN). Convolutional neural networks (CNNs) use complex feature extraction from EEG data, and Spider wasp optimization (SWO) improves network parameters by comparing them to the pursuing and developing behaviors of spider wasps. By enhancing classification accuracy, precision, recall, and F1-score measures, the combined effect seeks to achieve significant improvements over conventional techniques. The research concludes that the SW-CNN hybrid model performs better at differentiating between non-seizure activity and epileptic seizures. This strategy improves diagnosis accuracy and efficiency in public health, resulting in better patient outcomes and treatment planning. Effective transmission of such results is critical for furthering clinical diagnosis and research in neurological diseases.