How to build nomogram for type 2 diabetes using a naïve Bayesian classifier technique - Park, Jae-Cheol; Lee, Jea-Young (Journal of applied statistics, 2018)
In this study, we introduced a method for building a Bayesian nomogram and proposed an appropriate nomogram for type 2 diabetes (T2D) using data from 13,474 subjects collected from the 2013-2015 Korean National Health and Nutrition Examination Survey (KNHANES) data. We identified risk factors related to T2D, proposed a visual nomogram for T2D from a naïve Bayesian classifier model, and predicted incidence rates. Additionally, we computed confidence intervals for the influence of risk factors (attributes) and verified the proposed Bayesian nomogram using a receiver operating characteristic curve. Finally, we compared logistic regression and the Bayesian nomogram for T2D. The results of the analysis of the T2D data showed that the most influential factor among all attributes in the Bayesian nomogram was age group, and the highest risk factor for T2D incidence was cardiovascular disease. Dyslipidemia and hypertension also had significant impacts on T2D incidence while the effects of sex, smoking status, and employment status were relatively small compared to those of other variables. Using the proposed Bayesian nomogram, we can easily predict the incidence rate of T2D in an individual, and treatment plans can be established based on this information.