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Minerva Cardiology and Angiology 2021 May 04

DOI: 10.23736/S2724-5683.21.05637-4

Copyright © 2021 EDIZIONI MINERVA MEDICA

language: English

Machine learning analysis: general features, requirements and cardiovascular applications

Carlo RICCIARDI 1 , Renato CUOCOLO 2, Rosario MEGNA 3, Mario CESARELLI 4, 5, Mario PETRETTA 6

1 Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy; 2 Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy; 3 Institute of Biostructure and Bioimaging, National Council of Research, Naples, Italy; 4 Department of Information Technology and Electrical Engineering, University of Naples Federico II, Naples, Italy; 5 Bioengineering Unit, Institute of Care and Scientific Research Maugeri, Pavia, Italy; 6 IRCCS SDN, Naples, Italy


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Artificial intelligence represents the science which will probably change the future of medicine by solving actually challenging issues. In this special article, the general features of machine learning are discussed. First, a background explanation regarding the division of artificial intelligence, machine learning and deep learning is given and a focus on the structure of machine learning subgroups is shown. The traditional process of a machine learning analysis is described, starting from the collection of data, across features engineering, modelling and till the validation and deployment phase. Due to the several applications of machine learning performed in literature in the last decades and the lack of some guidelines, the need of a standardization for reporting machine learning analysis results emerged. Some possible standards for reporting machine learning results are identified and discussed deeply; these are related to study population (number of subjects), repeatability of the analysis, validation, results, comparison with current practice. The way to the use of machine learning in clinical practice is open and the hope is that, with emerging technology and advanced digital and computational tools, available from hospitalization and subsequently after discharge, it will also be possible, with the help of increasingly powerful hardware, to build assistance strategies useful in clinical practice.


KEY WORDS: Artificial intelligence; Machine learning; Guidelines

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