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.
Samsung Advanced Institute of Technology, Korea. hayoung7.kim@samsung.com
Ha-Young
HY
Kim
Sanghyun
S
Yoo
Jihyun
J
Lee
Hye Jin
HJ
Kam
Kyoung-Gu
KG
Woo
Yoon-Ho
YH
Choi
Jidong
J
Sung
Mira
M
Kang
D000465:Algorithms; D001185:Artificial Intelligence; D002114:Calcinosis / Q000150:complications* / Q000175:diagnosis*; D003324:Coronary Artery Disease / Q000175:diagnosis* / Q000209:etiology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D018570:Risk Assessment