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Nomogram construction to predict dyslipidemia based on a logistic regression analysis

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  • Ju-Hyun Seo
  • Hyun-Ji Kim
  • Jea-Young Lee

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.

Suggested Citation

  • Ju-Hyun Seo & Hyun-Ji Kim & Jea-Young Lee, 2020. "Nomogram construction to predict dyslipidemia based on a logistic regression analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(5), pages 914-926, April.
    • Handle: RePEc:taf:japsta:v:47:y:2020:i:5:p:914-926
    DOI: 10.1080/02664763.2019.1660760
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