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Heart disease is now one of the leading causes of death. As a result of the high difficulty of diagnosing and treating heart disease, as well as a lack of effective medical diagnostic equipment, medical practitioners, and other services, the mortality rate is rising dramatically, especially in developing countries. Since many preventable heart diseases are on the rise, large-scale adequate preventive measures are needed. In today's digital world, many clinical decision support systems to prevent heart disease have been developed by various scientists to simplify and ensure effective diagnosis. This paper examines the state-of-the-art support systems for different clinical decisions to prevent heart disease. This support system is proposed by various researchers using different preprocessing (ECG signal denoising), feature extraction and classification techniques for disease diagnosis. [ABSTRACT FROM AUTHOR]