Convolutional neural network based data interpretable framework for Alzheimer's treatment planning.

ABSTRACT: Alzheimer's disease (AD) is a neurological disorder that predominantly affects the brain. In the coming years, it is expected to spread rapidly, with limited progress in diagnostic techniques. Various machine learning (ML) and artificial intelligence (AI) algorithms have been employed to detect AD using single-modality data. However, recent developments in ML have enabled the application of these methods to multiple data sources and input modalities for AD prediction. In this study, we developed a framework that utilizes multimodal data (tabular data, magnetic resonance imaging (MRI) images, and genetic information) to classify AD. As part of the pre-processing phase, we generated a knowledge graph from the tabular data and MRI images. We employed graph neural networks for knowledge graph

SUBMITTER: Parvin S 

PROVIDER: S-EPMC10830981 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature