Ha-young Kim

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.

Identifying relatively high-risk group of coronary artery calcification based on progression rate: Statistical and machine learning methods

Coronary artery calcification (CAC) score is an important predictor of coronary artery disease (CAD), which is the primary cause of death in advanced countries. Early prediction of high-risk of CAC based on progression rate enables people to prevent CAD from developing into severe symptoms and diseases. In this study, we developed various classifiers to identify patients in high risk of CAC using statistical and machine learning methods, and compared them with performance accuracy. For statistical approaches, linear regression based classifier and logistic regression model were developed. For machine learning approaches, we suggested three kinds of ensemble-based classifiers (best, top-k, and voting method) to deal with imbalanced distribution of our data set. Ensemble voting method outperformed all other methods including regression methods as AUC was 0.781.

Alzheimer disease detection and analysis using P3 component of ERP in Alzheimer type dementia

This study is to develop the Alzheimers disease (AD) detection and analysis system using event-related potential (ERP) of AD patients. We recorded ERP in an auditory oddball task in mild AD (n=25), severe AD (n=12), age-matched normal aged controls (n=17), and young controls (n=7). The amplitude and latency of target P3 components were compared among 4 groups. The relationship between P3 measures and neuro psychological test scores were evaluated by correlations. The latency of the target P3a and P3b was prolonged in AD and the effects were correlated with the severity of dementia. The P3 amplitude was not affected significantly in AD. Theres no difference between normal aged group and young group. These results suggest that the P3 component is specifically affected by Alzheimer type dementia.