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http://dx.doi.org/10.5351/KJAS.2019.32.4.619

Nomogram building to predict dyslipidemia using a naïve Bayesian classifier model  

Kim, Min-Ho (Department of Statistics, Yeungnam University)
Seo, Ju-Hyun (Department of Statistics, Yeungnam University)
Lee, Jea-Young (Department of Statistics, Yeungnam University)
Publication Information
The Korean Journal of Applied Statistics / v.32, no.4, 2019 , pp. 619-630 More about this Journal
Abstract
Dyslipidemia is a representative chronic disease affecting Koreans that requires continuous management. It is also a known risk factor for cardiovascular disease such as hypertension and diabetes. However, it is difficult to diagnose vascular disease without a medical examination. This study identifies risk factors for the recognition and prevention of dyslipidemia. By integrating them, we construct a statistical instrumental nomogram that can predict the incidence rate while visualizing. Data were from the Korean National Health and Nutrition Examination Survey (KNHANES) for 2013-2016. First, a chi-squared test identified twelve risk factors of dyslipidemia. We used a naïve Bayesian classifier model to construct a nomogram for the dyslipidemia. The constructed nomogram was verified using a receiver operating characteristics curve and calibration plot. Finally, we compared the logistic nomogram previously presented with the Bayesian nomogram proposed in this study.
Keywords
Dyslipidemia; risk factor; naive Bayesian classifier; nomogram;
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