Leveraging NoSQL Databases for Real-Time Analysis and Prediction of Cognitive Decline Using fMRI Data and Wearable Sensor Integration in Early Alzheimer's Detection

Abstract

"This thesis aims to highlight the efficiency of appropriate data structures in machine learning. It showcases the prediction of Alzheimer’s identification for early detection by enabling users to leverage commonly available data through a fine-tuned Large Language Model (LLM) for diagnostic purposes."

Using a simplistic, modular but organized website for data collection, health centers could have a better organization and proper follow up of patients health data, allowing LLMs be available to work as smart diagnostic tools, lowering the workload of health workers.