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.
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Acknowledgments
This work was supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia under grant III41007, and by EU H2020 SMARTool project, project ID: 689068. The authors also thank to SES, the world-leading operator of ASTRA satellites, for their kind support in presentation and publishing of this paper.
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Cirkovic, B.A., Isailovic, V., Nikolic, D., Saveljic, I., Parodi, O., Filipovic, N. (2018). Prediction of Coronary Plaque Progression Using Data Driven Approach. In: Fratu, O., Militaru, N., Halunga, S. (eds) Future Access Enablers for Ubiquitous and Intelligent Infrastructures. FABULOUS 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 241. Springer, Cham. https://doi.org/10.1007/978-3-319-92213-3_33
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DOI: https://doi.org/10.1007/978-3-319-92213-3_33
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