One of the major factors contributing to the rising death rate is cardiovascular disease. Analyzing clinical data has made it harder to predict cardiovascular disease. To solve the aforementioned problems, an improved DenseNet model is presented in this study. The proposed approach forecasts Central Retinal Artery Occlusion (CRAO) and Coronary Artery Disease (CAD) simultaneously by using the patient's data from eye and cardiac examinations. Then, the coherence relationship is calculated with the help of Pearson’s correlation coefficient for both diseases. As far as we are aware, this is the first study to use DL techniques to predict the coherence between CRAO and CAD. While predicting the CAD, Improved DenseNet 97.5% accuracy when compared with benchmarked DL models like ResNet 50 and VGG16.
Keywords
Cardiovascular Disease, Deep Learning, Median Filter, PCA (Principal Component Analysis), GAN (Generative Adversarial Network), Central Retinal Artery Occlusion (CRAO), Coronary Artery Disease (CAD), Improved Densenet.
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Nancy Lima Christy S
Department of Computer Science and Engineering, KGiSL Institute of Technology, Coimbatore, Tamil Nadu, India.
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Cite this article
Nancy Lima Christy S and Nithyakalyani S, “A Novel Approach Analysis of Heart and Eye Disease Coherence Detection using Deep Learning Techniques”, Journal of Machine and Computing. doi: 10.53759/7669/jmc202505002.
