The advancement of medical science and technology has yet not evolved up with a concrete solution towards early detection of cardiac arrest from practical deployment. It is noted that artificial intelligence (AI) has been proving a potential contributor to address this state of diagnosis emergency. In current era of research work, there has been various implementation model and review work has been carried out towards advocating AI for determining early onset of cardiac arrest; however, there are various contradiction and shortcoming which is quite challenging to be extracted. Hence, the current manuscript presents a review of existing methodology by presenting core taxonomies of recent AI-methods towards early detection of cardiac arrest. Various standard dataset has been studied too to find associated advantages and limitation that restrict the actual potential of AI to prediction. The outcome presents novel highlights of research gap, trade-off, and crisp highlights of effectiveness of existing AI approaches as a study contribution.
Venkatesha et al. (Mon,) studied this question.
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