ADVANCING DIABETES PREDICTION WITH GENERATIVE AI: A MULTI-OMICS AND DEEP LEARNING PERSPECTIVE

Abstract

Using deep neural network topologies, this study explores the identification of critical transition zones in complex biological systems, with a particular emphasis on disease progression in medical imaging and genomic data analysis. To effectively distinguish between healthy and pathological states, raw medical data such as MRI scans or genomic sequences—are encoded into a latent space using a Variational Autoencoder (VAE). By capturing important structural and functional features, this latent space facilitates the identification of disease markers without the need for predefined diagnostic parameters. Our approach predicts critical transition points with high accuracy and shows strong alignment with clinical expectations. This strategy bridges gaps in conventional medical diagnostic methods by integrating advanced machine learning techniques to uncover subtle patterns in complex biological systems. New applications and recent benchmarks confirm machine learning's capacity to reveal fundamental insights across various medical domains, including early disease detection, treatment response prediction, and personalized medicine.