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

Abstract

Currently, 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. The 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. Obtained 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. The KSC MR study results, if positive, could establish a 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.

Analysis of Risk Factors in Patients with Subclinical Atherosclerosis and Increased Cardiovascular Risk Using Factor Analysis

“…The idea of KSC MR is to use ML methods to analyze the characteristics of patients, including physical findings, laboratory examination, questionnaires, and, among other things, the results of selective coronarography. All the significant results from the processing and modeling of these data should be part of the software (in the form of DSSC), which will enable the early detection of high-risk patients concerning the possibilities of personalized medicine [ 12 ]. The KSC MR study plan was to enroll prospectively approximately over 1000 patients aged 18 and above, without an upper limit, who will be admitted to the associated hospital suspected of having coronary artery disease.…”