Alzheimer’s Disease Classification Using Gan (Generative Adversarial Networks) And Mpa (Marine Predators Algorithm)

Abstract

This project aims to develop an advanced machine learning model for the classification of Alzheimer’s using Electroencephalogram (EEG) data, combining Generative Adversarial Networks (GANs) for feature extraction and pattern recognition with optimization algorithms, including the Marine Predators Algorithm(MPA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Ant Colony Optimization (ACO), for enhancing model performance. These optimization algorithms are employed to fine-tune the hyperparameters of the GAN model, ensuring improved classification accuracy and generalization. The approach helps in making improvements in classification accuracy, precision, recall, and F1-score, outperforming traditional machine learning models. The user interface (UI) designed in such a way that the users upload the Electroencephalogram (EEG) data in csv file and know that whether they have Alzheimer's Disease (AD) or not.  By incorporating these advanced optimization techniques, the system provides a reliable, automated tool that can assist healthcare professionals in early diagnosis and treatment planning of Alzheimer’s disease with greater efficiency and precision.