Metabolic syndrome can cause complications, such as stroke and cardiovascular disease. We aimed to propose a nomogram that visualizes and predicts the probability of metabolic syndrome occurrence after identifying risk factors related to metabolic syndrome for prevention and recognition. We created a nomogram related to metabolic syndrome in this paper for the first time. We analyzed data from the Korea National Health and Nutrition Examination Survey VII. Total 17,584 participants were included in this study, and the weighted sample population was 39,991,680, which was 98.1% of the actual Korean population in 2018. We identified 14 risk factors affecting metabolic syndrome using the Rao-Scott chi-squared test. Next, logistic regression analysis was performed to build a model for metabolic syndrome and 11 risk factors were finally obtained, including BMI, marriage, employment, education, age, stroke, sex, income, smoking, family history and age* sex. A nomogram was constructed to predict the occurrence of metabolic syndrome using these risk factors. Finally, the nomogram was verified using a receiver operating characteristic curve (ROC) and a calibration plot.

Metabolic syndrome is a serious disease that can eventually lead to various complications, such as stroke and cardiovascular disease. In this study, we aimed to identify the risk factors related to metabolic syndrome for its prevention and recognition and propose a nomogram that visualizes and predicts the probability of the incidence of metabolic syndrome. We conducted an analysis using data from the Korea National Health and Nutrition Survey (KNHANES VII) and identified 10 risk factors affecting metabolic syndrome by using the Rao-Scott chi-squared test, considering the characteristics of the complex sample. A naïve Bayesian classifier was used to build a nomogram for metabolic syndrome. We then predicted the incidence of metabolic syndrome using the nomogram. Finally, we verified the nomogram using a receiver operating characteristic curve and a calibration plot.

In medical research, the risk factors associated with human diseases need to be identified to predict the incidence rate and determine the treatment plan. Logistic regression analysis is primarily used in order to select risk factors. However, individuals who are unfamiliar with statistics outcomes have trouble using these methods. In this study, we develop a nomogram that graphically represents the numerical association between the disease and risk factors in order to identify the risk factors for delirium and to interpret and use the results more effectively. By using the logistic regression model, we identify risk factors related to delirium, construct a nomogram and predict incidence rates. Additionally, we verify the developed nomogram using a receiver operation characteristics (ROC) curve and calibration plot. Nursing home, stroke/epilepsy, metabolic abnormality, hemodynamic instability, and analgesics were selected as risk factors. The validation results of the nomogram, built with the factors of training set and the test set of the AUC showed a statistically significant determination of 0.893 and 0.717, respectively. As a result of drawing the calibration plot, the coefficient of determination was 0.820. By using the nomogram developed in this paper, health professionals can easily predict the incidence rate of delirium for individual patients. Based on this information, the nomogram could be used as a useful tool to establish an individual's treatment plan.