Objective: To establish models to predict the risk of dyslipidemia in individuals, and then to explore and evaluate new prediction models.
Methods: The epidemiological survey data of 8914 community residents was selected and divided into a trained group (6686 cases) and a test group (2228 cases). Artificial neural network (ANN) and Logistic regression analysis were used to establish prediction models, and then the results were evaluated by receiver operating characteristic (ROC) curve.
Results: The specificity (64.79%) of AAN model forecasting the results of test group was lower, but the sensitivity (94.86%), Youden's index (59.65%) and consistency rate (81.23%) of AAN model was higher than the Logistic regression predicted model (specificity = 77.49%, sensitivity = 53.51%, Youden' s index = 31.00% and consistency rate = 81.23%, respectively). Moreover, the area under ROC curve of ANN prediction model (Az = 0.824 +/- 0.009) was significantly bigger than the Logistic regression prediction model (Az = 0. 655 +/- 0.012).
Conclusion: The discrimination performance of ANN model is better than Logistic regression in the prediction of health risk of dyslipidemia in individuals.