Deep Learning Framework for Early Detection of Heart Attack Risk and Cardiovascular Conditions using Retinal Images

In today’s world, cardiovascular diseases including heart attacks are the prime reason for mortality. Detecting heart attack risk at early stages and accurate assessment of such risks related to cardiovascular conditions play an essential role in active patient management and preventive interventions. In the current research work, a novel deep learning framework called "CardioSightFrame" is proposed for detecting heart attack risks and cardiovascular conditions at early stages using retinal images. The proposed algorithm integrates Graph Convolutional Networks (GCNs) and Vision Transformers (ViTs) to control both local structural information and global contextual understanding from the retinal scans. A comprehensive simulation analysis is conducted to evaluate the performance of CardioSightFrame and it is compared with the existing algorithms. Also, the predictive accuracy of the proposed algorithm is measured using appropriate simulation metrics. By treating retinal images as graph nodes and leveraging self-attention mechanisms, the proposed algorithm achieves accurate and efficient feature extraction which is vital for early detection of heart attack risk and cardiovascular conditions. To evaluate CardioSightFrame’s performance, wide-ranging simulation experiments are conducted using a varied dataset of retinal images. Through these comparisons, the predictive accuracy, sensitivity, specificity, and computational efficiency of CardioSightFrame are established. The simulation results indicate that CardioSightFrame outclasses existing algorithms in terms of accuracy and sensitivity for early detection of heart attack risk and cardiovascular conditions. The algorithm’s ability to efficiently investigate retinal images with reduced computational overhead further enhances its applied pertinence.