An Artificial Neural Network Model Combined with Dietary Retinol Intake from Different Sources to Predict the Risk of Nonalcoholic Fatty Liver Disease

Abstract

ObjectiveThis study aimed to develop an artificial neural network (ANN) model combined with dietary retinol intake from different sources to predict the risk of non-alcoholic fatty liver disease (NAFLD) in American adults. MethodsData from the 2007 to 2014 National Health and Nutrition Examination Survey (NHANES) 2007–2014 were analyzed. Eligible subjects (n = 6,613) were randomly divided into a training set (n1 = 4,609) and a validation set (n2 = 2,004) at a ratio of 7:3. The training set was used to identify predictors of NAFLD risk using logistic regression analysis. An ANN was established to predict the NAFLD risk using a training set. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the accuracy of the model using the training and validation sets. ResultsOur study found that the odds ratios (ORs) and 95% confidence intervals (CIs) of NAFLD for the highest quartile of plant-derived dietary retinol intake (i.e., provitamin A carotenoids, such as β-carotene) (OR = 0.75, 95% CI: 0.57 to 0.99) were inversely associated with NAFLD risk, compared to the lowest quartile of intake, after adjusting for potential confounders. The areas under the ROC curves were 0.874 and 0.883 for the training and validation sets, respectively. NAFLD occurs when its incidence probability is greater than 0.388. ConclusionThe ANN model combined with plant-derived dietary retinol intake showed a significant effect on NAFLD. This could be applied to predict NAFLD risk in the American adult population when government departments formulate future health plans.