A Review: Heart Disease Prediction in Machine Learning & Deep Learning

Abstract

The whole world has come to know the fact that heart disease is not a trivial issue. Although the years have changed, throngs of patients are diagnosed with this lethal disease and not only is it not decreasing, but increasing and this is evident from the analysis of death rates across the country. Heart disease is caused by several main factors such as negligence in taking care of diet and daily life. Causes such as age and genetics cannot be fully controlled by humans. So it is the responsibility of each individual to take care of critical risk factors that can be controlled by humans. Innovation and data mining technology should also help to reduce the death rate caused by heart disease. Existing data should be used to find hidden relationships, which are very useful for early diagnosis, which can be a new advanced technology and potentially improve the level of the national health system. This paper underlines previous studies that leverage machine learning and deep learning expertise to predict heart disease. Each algorithm produces variations in accuracy and this study will examine the variables that enable these results. At the end of the study, the results achieved by deep learning proved to be ahead of machine learning by showing consistency in a satisfactory range between 84% to 99%.

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