Cardiovascular disease prediction model based on patient behavior patterns in the context of deep learning: a time-series data analysis perspective.

Abstract

To address the limitations of traditional cardiovascular disease prediction models in capturing dynamic changes and personalized differences in patients, we propose a novel LGAP model based on time-series data analysis. This model integrates Long Short-Term Memory (LSTM) networks, Graph Neural Networks (GNN), and Multi-Head Attention mechanisms. By combining patients' time-series data (such as medical records, physical parameters, and activity data) with relationship graph data, the model effectively identifies patient behavior patterns and their interrelationships, thereby improving the accuracy and generalization of cardiovascular disease risk prediction. Experimental results show that LGAP outperforms traditional models on datasets such as PhysioNet and NHANES, particularly in prediction accuracy and personalized health management. The introduction of LGAP offers a new approach to enhancing the precision of cardiovascular disease prediction and the development of customized patient care plans.