Machine Learning Algorithms in Cardiology Domain: A Systematic Review (Preprint)

Abstract

<sec> <title>BACKGROUND</title> It has been shown in previous decades, that Machine Learning (ML) has a huge variety of possible implementations in medicine and can be very helpful. Neretheless, cardiovascular diseases causes about third of of all global death. Does ML work in cardiology domain and what is current progress in that regard? </sec> <sec> <title>OBJECTIVE</title> The review aims at (1) identifying studies where machine-learning algorithms were applied in the cardiology domain; (2) providing an overview based on identified literature of the state of the art of the ML algorithm applying in cardiology. </sec> <sec> <title>METHODS</title> For organizing this review, we have employed PRISMA statement. PRISMA is a set of items for reporting in systematic reviews and meta-analyses, focused on the reporting of reviews evaluating randomized trials, but can also be used as a basis for reporting systematic review. For the review, we have adopted PRISMA statement and have identified the following items: review questions, information sources, search strategy, selection criteria. </sec> <sec> <title>RESULTS</title> In total 27 scientific articles or conference papers written in English and reporting about implementation of an ML-method or algorithm in cardiology domain were included in this review. We have examined four aspects: aims of ML-systems, methods, datasets and evaluation metrics. </sec> <sec> <title>CONCLUSIONS</title> We suppose, this systematic review will be helpful for researchers developing machine-learning system for a medical domain and in particular for cardiology. </sec>

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