Neural network approach to predict the association between blood cadmium levels and hypertension

Hypertension is a global health concern and a major risk factor for cardiovascular disease. Early prevention and management based on risk prediction is a principal goal of many national health policies. We studied the relationship between blood cadmium concentrations and hypertension and developed an artificial neural network (ANN) that predicts hypertension risk. For this study, we utilized data from the Korean National Health and Nutrition Examination Survey (KNHANES), conducted between 2008 and 2013, which is a nationwide population-based survey of the Korean population. We extracted and analyzed sociodemographic characteristics, serum cadmium levels, and blood pressure information from a sample of adults aged 19 years and above (n=11,530). After adjusting for sociodemographic factors, cadmium levels were positively associated with the risk of hypertension (p < 0.001). Groups with high cadmium levels significantly increased the odds ratios for hypertension compared to the lowest tertile. An ANN model in which sociodemographic factors and the blood concentration of cadmium were the principal inputs yielded a predictive accuracy of 0.773 and an area under the curve of 0.823. ANNs with appropriate inputs can identify population subgroups at high risk of developing hypertension and will aid in the formulation of policies that prevent disease.