A Systematic Analysis for Cardiovascular Disease Classification Using Deep Learning

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Abstract

The processing of medical data has profited from automation and contemporary computing breakthroughs, which have given rise to several novel instructional approaches. In both traditional and cutting-edge disciplines, deep learning has emerged as a cutting-edge machine learning paradigm. Deep learning algorithms have developed into supervised, semi-supervised, and unsupervised modes for a variety of real-time applications. The technology has shown to be broadly applicable in image processing, computer vision, medical diagnostics, robotics, and control operation. Deep learning is essential in the field of medical science for recognizing various diseases and solving health issues. The deep learning revolution will significantly alter cardiovascular imaging within the next decade. To ensure that deep learning may meaningfully impact clinical practice, it is crucial for medical practitioners to stay up with these breakthroughs. This evaluation is intended to be a preliminary step in that process. In this study cardiovascular disease diagnosis and classification have been examined using different state-of-the-art deep learning approaches. Deep Neural Networks can be used to improve the classification of heart disease as a whole in a crucial field called heart illness. The classification process can be carried out using a variety of methods, such as DL, KNN, SVM, ANN Nave Bayes, Random Forest, SSD, DNN, and TDNN where DL exhibited maximum accuracy. Although deep learning-based automated cardiovascular classification algorithms have shown highly accurate results, they have not yet been widely used by healthcare professionals.

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