Machine learning and deep learning techniques for the analysis of heart disease: a systematic literature review, open challenges and future directions

Myocardial infarction, commonly known as heart attack, is one of the most common heart diseases prevailing in the human world. Heart or cardiac disease is one of the leading causes of human deaths. It is observed that cardiac arrest or cardiac disease mostly develop over time but are hard to discover due to the lack of knowledge and technology, mostly in developing countries. Even though these are preventable, the lack of experience and equipment is one of the leading factors for such a high death rate. In this study, we will discuss different practices used for the analysis of various heart diseases using Machine Learning (ML) and Deep Learning (DL) algorithms such as Convolutional Neural Networks (CNNs), recurrent neural networks, deep belief networks, long short-term memory, and others investigated by different researchers over the time span. The articles, for this study, were considered from 2018 to 2022 and after the screening, 63 articles were used for primary study. This systematic literature review on analysing heart diseases will help the future researchers to understand the pre-existing ML and DL practices in the healthcare industry. It gives an insight of the prominent techniques such as random forest, support vector machine, CNNs, decision tree, and so on. It also discusses the popular datasets used for the deployment of numerous diagnostic models. It also highlights the popular publishers along with journals and conferences from where the literature can be analysed. Further, it will help them in comprehending the existing open issues or challenges faced by the previous researchers. The most common issue was the unavailability of larger and discrete datasets followed by the improvement of the pre-existing models.