Alzheimer Disease Prediction

Alzheimer’s disease is a progressive neurodegenerative disorder that severely impacts memory,cognitive function, and daily living activities. With its global prevalence rising, early diagnosis has becomecritical for effective intervention and management. This study presents a robust, AI-driven web-basedsystem designed to predict Alzheimer’s disease at early stages using advanced deep learning methodologies.Leveraging the power of Convolutional Neural Networks (CNNs), the system analyzes brain scan imagesto detect subtle patterns indicative of early neuronal degeneration. A secure backend infrastructure utilizingSupabase for authentication and MongoDB for efficient data management ensures data privacy and aseamless user experience. Additionally, the platform integrates an intelligent AI chatbot that providesinteractive support and guidance to users throughout the diagnostic process. Real-time data visualizationtools offer clinicians and patients an intuitive understanding of prediction outcomes, fostering moreinformed medical decision-making. Extensive testing on publicly available datasets demonstrates thesystem’s high accuracy, reliability, and scalability. Furthermore, the architecture is modular and flexible,allowing future integration with other diagnostic tools and telemedicine services. By bridging advancedmachine learning techniques with accessible digital platforms, this project aims to democratize earlyAlzheimer’s detection and pave the way for proactive healthcare interventions, ultimately improving patientoutcomes and reducing societal burdens.