Epilepsy Prevention Using Enhanced Genetic Algorithm Tuned Multilayer Network

This paper presents a novel methodology for epileptic seizure prediction by integrating a genetic algorithm (GA) with a multilayer neural network (MLNN) to optimize both architectural and hyperparameter configurations. The proposed framework aims to enhance predictive accuracy by leveraging the GA's global search capabilities to identify optimal network structures, thereby addressing the limitations of manual tuning and local minima in traditional training methods. This synergy enables the neural network to effectively learn complex, non-linear patterns inherent in electroencephalogram (EEG) signals, which are critical for early seizure detection. By enabling timely and accurate prediction of seizure onset, the approach offers significant potential for proactive clinical interventions and improved patient quality of life. Experimental results demonstrate that the GA-optimized MLNN outperforms conventional models in terms of prediction accuracy and robustness, highlighting its applicability in real-world neurological monitoring systems