  Heart disease, also known as cardiovascular disease, is a prevalent and
critical medical condition characterized by the impairment of the heart and
blood vessels, leading to various complications such as coronary artery
disease, heart failure, and myocardial infarction. The timely and accurate
detection of heart disease is of paramount importance in clinical practice.
Early identification of individuals at risk enables proactive interventions,
preventive measures, and personalized treatment strategies to mitigate the
progression of the disease and reduce adverse outcomes. In recent years, the
field of heart disease detection has witnessed notable advancements due to the
integration of sophisticated technologies and computational approaches. These
include machine learning algorithms, data mining techniques, and predictive
modeling frameworks that leverage vast amounts of clinical and physiological
data to improve diagnostic accuracy and risk stratification. In this work, we
propose to detect heart disease from ECG images using cutting-edge
technologies, namely vision transformer models. These models are Google-Vit,
Microsoft-Beit, and Swin-Tiny. To the best of our knowledge, this is the
initial endeavor concentrating on the detection of heart diseases through
image-based ECG data by employing cuttingedge technologies namely, transformer
models. To demonstrate the contribution of the proposed framework, the
performance of vision transformer models are compared with state-of-the-art
studies. Experiment results show that the proposed framework exhibits
remarkable classification results.
