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New Model for Predicting the Presence of Coronary Artery Calcification.

Abstract：

:Coronary artery calcification (CAC) is a feature of coronary atherosclerosis and a well-known risk factor for cardiovascular disease (CVD). As the absence of CAC is associated with a lower incidence rate of CVD, measurement of a CAC score is helpful for risk stratification when the risk decision is uncertain. This was a retrospective study with an aim to build a model to predict the presence of CAC (i.e., CAC score = 0 or not) and evaluate the discrimination and calibration power of the model. Our data set was divided into two set (80% for training set and 20% for test set). Ten-fold cross-validation was applied with ten times of interaction in each fold. We built prediction models using logistic regression (LRM), classification and regression tree (CART), conditional inference tree (CIT), and random forest (RF). A total of 3,302 patients from two cohorts (Soonchunhyang University Cheonan Hospital and Kangbuk Samsung Health Study) were enrolled. These patients' ages were between 40 and 75 years. All models showed acceptable accuracies (LRM, 70.71%; CART, 71.32%; CIT, 71.32%; and RF, 71.02%). The decision tree model using CART and CIT showed a reasonable accuracy without complexity. It could be implemented in real-world practice.

journal_name

J Clin Med

journal_title

Journal of clinical medicine

authors

Park S,Hong M,Lee H,Cho NJ,Lee EY,Lee WY,Rhee EJ,Gil HW

doi

10.3390/jcm10030457

subject

Has Abstract

pub_date

2021-01-25 00:00:00

issue

3

issn

2077-0383

pii

jcm10030457

journal_volume

10

pub_type

杂志文章

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