Prediction of 4-year risk for coronary artery calcification using ensemble-based classification

The progression of coronary artery calcification (CAC) has been regarded as an important risk factor of coronary artery disease (CAD), which is the biggest cause of death. Because CAC occurrence increases the risk of CAD by a factor of ten, the one whose coronary artery is calcified should pay more attention to the health management. However, performing the computerized tomography (CT) scan to check if coronary artery is calcified as a regular examination might be inefficient due to its high cost. Therefore, it is required to identify high risk persons who need regular follow-up checks of CAC or low risk ones who can avoid unnecessary CT scans. Due to this reason, we develop a 4-year prediction model for a new occurrence of CAC based on data collected by the regular health examination. We build the prediction model using ensemble-based methods to handle imbalanced dataset. Experimental results show that the developed prediction models provided a reasonable accuracy (AUC 75%), which is about 5% higher than the model built by the other imbalanced classification method.