Predicting the Onset of Alzheimer’s Disease using Machine Learning and Deep Learning Techniques

Alzheimer’s Disease (AD) represents a crucial challenge in healthcare, necessitating effective diagnostic tools and therapeutic interventions. In an effort to promote early identification and intervention, this research describes a unique method for predicting the onset of AD using machine learning and deep learning approaches. Leveraging advanced computational methods, the proposed model analyses MRI data to identify individuals at risk of developing AD. Through data preprocessing, feature extraction, and model development, the proposed model demonstrates the efficacy of Convolutional Neural Networks (CNNs), Random Forest classifiers, Support Vector Machines (SVMs), Gradient Boosting Machines (GBMs), and ensemble learning methods in accurately detecting AD from MRI scans. The deployment of user-friendly web applications enables seamless interaction between healthcare professionals and predictive models, empowering proactive interventions in diverse clinical settings. This research contributes to bridging the gap between cutting-edge technology and clinical practice, offering a transformative approach to AD diagnosis and management.