Key result
Deep learning models achieve ~98% accuracy in detecting and predicting heart disease.
Why the study?
Heart disease is a leading cause of death worldwide, creating a critical need for improved cardiac disease diagnosis and an understanding of the challenges in applying deep learning methods.
Deep learning algorithms, specifically CNNs, show potential for improving the diagnostic accuracy and classification of cardiovascular diseases.
May support CNN use in cardiac diagnostics; leaves open prospective validation before clinical adoption.
Heart disease it is one of the main causes of death in the globe. Heart illness encompasses a spectrum of disorders that impact the heart, its blood arteries, and its overall functionality. Also referred to as cardiovascular disease. This paper investigates the potential benefits of deep learning (DL) architectures for improving diagnostic accuracy addressing the critical need for improved diagnosis of cardiac disease, and the difficulties associated with applying DL methods for heart disease identification. This survey study highlights the important role that DL plays in cardiovascular diagnostics from a number of tasks like as diagnosing, predicting, and classifying heart diseases. Convolutional Neural Networks (CNNs), a type of deep learning, are being used in the context of heart illness with the primary goal of creating accurate and dependable models for the identification, diagnosis, and prognosis of various heart-related disorders.
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Jalal et al. (2024) conducted a review in Heart Disease. Deep Learning Algorithms was evaluated. Deep learning architectures, particularly Convolutional Neural Networks, demonstrate high accuracy, often exceeding 98%, in detecting, diagnosing, and predicting various heart diseases.
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