Previously submitted to: JMIR Medical Informatics (no longer under consideration since Sep 19, 2019)
Date Submitted: May 22, 2019
Open Peer Review Period: May 27, 2019 - Jun 19, 2019
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Machine Learning Algorithms in Cardiology Domain: A Systematic Review
Background:
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?
Objective:
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.
Methods:
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.
Results:
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.
Conclusions:
We suppose, this systematic review will be helpful for researchers developing machine-learning system for a medical domain and in particular for cardiology.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.