• Arch Med Sci · Jan 2022

    The possible role of machine learning in detection of increased cardiovascular risk patients - KSC MR Study (design).

    • Daniel Pella, Stefan Toth, Jan Paralic, Jozef Gonsorcik, Jan Fedacko, Peter Jarcuska, Dominik Pella, Zuzana Pella, Frantisek Sabol, Monika Jankajova, Gabriel Valocik, Alina Putrya, Andrea Kirschová, Lukas Plachy, Miroslava Rabajdova, Mikulas Hunavy, Bibiana Kafkova, Ivan Doci, Silvia Timkova, Mariana Dvorožňáková, Frantisek Babic, Peter Butka, Lucia Dimunova, Maria Marekova, Zuzana Paralicova, Jaroslav Majernik, Jan Luczy, Jakub Janosik, and Martin Kmec.
    • 2 Department of Cardiology, Faculty of Medicine, Pavol Jozef Safarik University and East Slovak Institute of Cardiovascular Diseases, Košice, Slovak Republic.
    • Arch Med Sci. 2022 Jan 1; 18 (4): 991-997.

    IntroductionCurrently, just a few major parameters are used for cardiovascular (CV) risk quantification to identify many of the high-risk subjects; however, they leave a lot of them with an underestimated level of CV risk which does not reflect the reality.Material And MethodsThe submitted study design of the Kosice Selective Coronarography Multiple Risk (KSC MR) Study will use computer analysis of coronary angiography results of admitted patients along with broad patients' characteristics based on questionnaires, physical findings, laboratory and many other examinations.ResultsObtained data will undergo machine learning protocols with the aim of developing algorithms which will include all available parameters and accurately calculate the probability of coronary artery disease.ConclusionsThe KSC MR study results, if positive, could establisha base for development of proper software for revealing high-risk patients, as well as patients with suggested positive coronary angiography findings, based on the principles of personalised medicine.Copyright: © 2020 Termedia & Banach.

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