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Title: Prediction of coronary plaque progression using data driven approach
Authors: Andjelkovic-Cirkovic B.
Isailovic, Velibor
Nikolic, Dalibor
Saveljic I.
Parodi O.
Filipovic, Nenad
Journal: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Issue Date: 1-Jan-2018
Abstract: © 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Coronary artery disease or coronary atherosclerosis (CATS) is the most common type of cardiovascular disease and the number one cause of death worldwide. Early identification of patients who will develop progression of disease is beneficial for treatment planning and adopting the strategy for reduction of risk factors that could cause future cardiac events. In this paper, we propose the data mining model for prediction of CATS progression. We exploit patient’s health record by using various machine learning methods. Predictor variables, including heterogenious data from cellular to the whole organism level, are initially preprocessed by feature selection approaches to select only the most informative features as inputs to machine learning algorithms. Results obtained and features selected within this study indicate the high potential of machine learning to be used in clinical practice as well as that specific monocytes are important markers impacting the plaque progression.
Type: Conference Paper
DOI: 10.1007/978-3-319-92213-3_33
ISSN: 18678211
SCOPUS: 85049809789
Appears in Collections:Faculty of Engineering, Kragujevac
Institute for Information Technologies, Kragujevac
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