Nomogram construction to predict dyslipidemia based on a logistic regression analysis

Ju-Hyun Seo, Hyun-Ji Kim and Jea-Young Lee

Journal of Applied Statistics, 2020, vol. 47, issue 5, 914-926

Abstract: Dyslipidemia is a chronic disease requiring continuous management and is a well-known risk factor for cardiovascular diseases as well as hypertension and diabetes. However, no studies have so far visualized and predicted the probability of dyslipidemia. Hence, this study proposes a nomogram based on a logistic regression model that can visualize its risk factors and predict the probability of developing dyslipidemia. Twelve risk factors for dyslipidemia are identified through a chi-squared test. We then conduct a logistic regression analysis with two interaction variables to obtain a model and build a nomogram for dyslipidemia. Finally, we verify the constructed nomogram using a receiver operation characteristic curve and calibration plot.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:47:y:2020:i:5:p:914-926

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DOI: 10.1080/02664763.2019.1660760

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