Vision Transformers in Early Detection of Cardiovascular Diseases

Heart disease remains a leading cause of mortality globally, ne-cessitating advancements in diagnostic methodologies to enhance early detection and treatment outcomes. This paper presents a novel approach to heart disease detection utilizing echocardiogram videos, which are rich in dynamic and morphological information and critical for accurate diagnostics. We propose an integrated model that combines Vision Transformer (ViT) models and Convolutional Neural Networks (CNN), for predicting heart disease which involves leveraging the sequential processing power of ViTs and the spatial feature extraction capabilities of CNNs. This hybrid approach is designed to improve the precision of heart function assessments by analyzing both spatial and temporal as-pects of echocardiographic data. Our model demonstrates significant improvements in detection rates compared to traditional methods, offering a promising tool for healthcare providers in the early diagnosis and management of heart disease, and achieving 98.28% training accuracy, and 96.33% validation accuracy. The effectiveness of this model is validated through comprehensive testing against established benchmarks, showing marked en-hancements in accuracy and reliability.