Comprehensive Deep Learning Framework for Disease Prediction

Abstract

 This study introduces an innovative hybrid deep learning framework designed to improve the precision of disease prediction by utilizing temporal data from Electronic Health Records (EHRs). The framework combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks, harnessing CNNs' ability to extract hierarchical feature representations from intricate data and LSTMs' strength in capturing long-term dependencies in temporal information. Empirical analysis using real-world EHR datasets demonstrated that this hybrid network significantly outperforms Support Vector Machine (SVM) models, as well as standalone CNNs and LSTMs, in disease prediction tasks. This research not only enhances predictive model performance in health data analytics but also highlights the critical role of advanced deep learning technologies in managing the complexity of contemporary medical data. Our results suggest a paradigm shift towards integrating sophisticated neural network architectures in predictive model development, potentially paving the way for more personalized and proactive disease management and care, thus setting new benchmarks for future health management practices.