Abstract

Heart disease remains a leading cause of mortality worldwide, necessitating innovative approaches for early detection and prevention. This study proposes a novel framework for predicting heart disease through the analysis of retinal images, leveraging the intricate relationship between ocular biomarkers and cardiovascular health. Utilizing advanced image processing techniques and deep learning algorithms, we develop a convolutional neural network (CNN) model to extract relevant features from retinal scans. The model is trained on a diverse dataset that includes retinal images labeled for heart disease presence and absence. We evaluate the model’s performance based on accuracy, sensitivity, and specificity, demonstrating its potential to serve as a non-invasive diagnostic tool. This research not only highlights the feasibility of utilizing retinal imaging for cardiovascular risk assessment but also aims to facilitate timely medical interventions, ultimately improving patient outcomes. Future work will focus on refining the model and exploring the integration of additional clinical data to enhance predictive accuracy.

Keywords:
Dataset of heart disease,
Convolutional Neural Network (CNN)

Introduction:

Cardiovascular diseases (CVDs) continue to be one of the leading causes of morbidity and mortality worldwide, significantly impacting public health. Early detection and timely intervention are critical for managing heart disease, as they can significantly improve patient outcomes and reduce healthcare costs. Traditional diagnostic methods for heart disease often rely on invasive procedures, extensive laboratory tests, and costly imaging techniques. Consequently, there is a growing need for non-invasive, cost-effective diagnostic approaches that can facilitate early screening and risk assessment.
Recent advances in medical imaging and machine learning have opened new avenues for integrating artificial intelligence (AI) with healthcare. Retinal images, captured through non-invasive techniques such as fundus photography, contain valuable information about an individual’s vascular health, offering insights into their cardiovascular condition. The retina's rich vascular network closely resembles that of the heart and other organs, allowing clinicians to infer systemic health conditions from ocular assessments. Abnormalities in retinal blood vessels, such as changes in size, shape, and branching patterns, can serve as important biomarkers for cardiovascular diseases.
In this study, we explore the potential of using deep learning algorithms to analyze retinal images for predicting heart disease. Specifically, we employ various convolutional neural network (CNN) architectures, including standard 2D CNNs, Separable CNNs, and Depthwise 2D CNNs, to capture intricate features in retinal scans. The objective is to develop a robust predictive model that can accurately distinguish between individuals with and without heart disease based on their retinal characteristics.

Our findings demonstrate that the Depthwise 2D CNN architecture exhibits superior performance in terms of accuracy compared to the other models. This can be attributed to its efficient feature extraction capabilities, which enable it to effectively capture the nuances of retinal images while maintaining lower computational demands. By leveraging such advanced deep learning techniques, we aim to provide a non-invasive, reliable, and cost-effective solution for early heart disease prediction.

This research not only highlights the feasibility of utilizing retinal imaging as a diagnostic tool for cardiovascular risk assessment but also underscores the critical role of machine learning in advancing healthcare solutions. By integrating AI into clinical practice, we can pave the way for more personalized and proactive healthcare strategies, ultimately improving patient care and outcomes in the fight against heart disease.