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A study on the method of constructing a nomogram for predicting dyslipidemia
Journal of the Korean Data & Information Science Society 2019;30:1063-75
Published online September 30, 2019;  https://doi.org/10.7465/jkdi.2019.30.5.1063
© 2019 Korean Data and Information Science Society.

Min-Ho Kim1 · Ju-Hyun Seo2 · Jea-Young Lee3

123Department of Statistics, Yeungnam University
Correspondence to: Professor, Department of Statistics, Yeungnam University, Gyeungsan 38541, Korea. E-mail: jlee@yu.ac.kr
Received July 15, 2019; Revised September 11, 2019; Accepted September 11, 2019.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Dyslipidemia is a chronic vascular disease that is difficult to diagnose without medical examination. There is a growing need to recognize and prevent dyslipidemia. Therefore, this study constructed the nomogram using logistic regression model and naive Bayesian classifier model to predict dyslipidemia. Furthermore, we compare the two nomograms. The data were used for adults over 20 years of age in the data of the Korean national health and nutrition examination survey for 2013-2016. Receiver operating characteristic (ROC) curves and calibration plots were used to verify the two nomograms. Finally, we compared the logistic nomogram with the Bayesian nomogram and presented their opinions. The left-aligned method was applied to Bayesian nomograms to facilitate comparison of two nomograms.
Keywords : Dyslipidemia, logisitic regression, naive Bayesian classifier, nomogram.