Implementing a Graph Convolutional Network (GCN) model for the early detection of Alzheimer's disease using a single neurodegeneration indicator

This research presents a diagnosis model of single biomarker of Alzheimer’s diseases using graph convolutional network (GCN). Alzheimer’s disease is quite a challenging disease to diagnose early and accurately due to its complexity and progression nature. Traditional approaches used troponin and various other enzymes as well as clinical procedures such as coronary imaging – techniques that are expensive and require time. This approach makes diagnosis less complex because it utilizes a single measure of neurodegeneration combined with GCNs, which rely on structural analysis of the brain’s neural network. The preclinical research entertaining requires assembling and standardizing a sizable amount of data to make certain that the chosen biomarker generates coherent signals of Alzheimer’s disease. To this end, the GCN model is designed to be able to discern between healthy and diseased states by fine-tuning graph convolutions to capture such nuances. Empirical results of key performance indicators show that using our model yields better results in diagnostic outcomes than conventional methods. This innovative approach not only brings down the diagnostic costs, but also helps diagnose Alzheimer’s faster and more accurately thus helping a patient’s condition, and quality of life.