Deep Learning and AI for Alzheimer’s Disease Prediction

Alzheimer disease (AD) is a degenerative condition of the nervous system that affects both cognition and memory. Early detection and prognosis of the illness are essential for effective disease management and therapy.Alzheimer’s disease is usually difficult to detect and can only be definitively identified post-mortem by study of brain tissue. Despite the importance of early diagnosis and treatment for improving patient outcomes, this illness is frequently difficult to identify. Alzheimer’s disease is diagnosed using a combination of clinical evaluation, cognitive testing, and neuroimaging techniques including MRI and PET scans. In this study, we offer a prediction model for Alzheimer’s disease on which Convolutional Neural Networks (CNN) are trained for deep learning (DL) and artificial intelligence (AI) technologies. Deep learning recognises and categorises AD based on criteria including growth, lifestyle, pace, and size. In medical science, both picture and text-based report-based data may have impurities such differing size, alignment, or consistency that lead to biased study results and complicated prediction and analysis. As an outcome, using CNN and Random Forest in a Python laboratory setting increases prediction accuracy to 86.91