Research Article
Prediction of Coronary Plaque Progression Using Data Driven Approach
@INPROCEEDINGS{10.1007/978-3-319-92213-3_33, author={Bojana Cirkovic and Velibor Isailovic and Dalibor Nikolic and Igor Saveljic and Oberdan Parodi and Nenad Filipovic}, title={Prediction of Coronary Plaque Progression Using Data Driven Approach}, proceedings={Future Access Enablers for Ubiquitous and Intelligent Infrastructures. Third International Conference, FABULOUS 2017, Bucharest, Romania, October 12-14, 2017, Proceedings}, proceedings_a={FABULOUS}, year={2018}, month={7}, keywords={Coronary artery disease Atherosclerosis progression Machine learning Feature selection}, doi={10.1007/978-3-319-92213-3_33} }- Bojana Cirkovic
Velibor Isailovic
Dalibor Nikolic
Igor Saveljic
Oberdan Parodi
Nenad Filipovic
Year: 2018
Prediction of Coronary Plaque Progression Using Data Driven Approach
FABULOUS
Springer
DOI: 10.1007/978-3-319-92213-3_33
Abstract
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.
