Prediction of Coronary Plaque Progression Using Data Driven Approach

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.