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

Can Liu; Shi Hui Zhou; Hong SU; Wen Qin Yang; Jiao LU

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

Author: Can LIU ^{1
,

2} ; Shi Hui ZHOU ³ ; Hong SU ⁴ ; Wen Qin YANG ⁵ ; Jiao LU ⁶
Author Information

1. School of Public Health, Shanxi Medical University, Taiyuan 030001, Shanxi, China
2. School of Management, Shanxi Medical University, Taiyuan 030001, Shanxi, China.
3. Department of Medical Administration, Affiliated Qingdao Central Hospital of Qingdao University, Qingdao 266000, Shandong, China.
4. Primary Medical Department, Affiliated Qingdao Central Hospital of Qingdao University, Qingdao 266000, Shandong, China.
5. Digestive Endoscopy Minimally Invasive Surgery Center, Shanxi Provincial Cancer Hospital, Chinese Academy of Medical Sciences Cancer Hospital Shanxi Hospital, Affiliated Cancer Hospital of Shanxi Medical University, Taiyuan 030013, Shanxi, China.
6. School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China.
Publication Type:Journal Article
Keywords: ANN; NAFLD; NHANES; Plant-derived dietary retinol intake; Prediction model
MeSH: Adult; Humans; Vitamin A; Non-alcoholic Fatty Liver Disease/epidemiology*; Nutrition Surveys; Diet; Neural Networks, Computer
From: Biomedical and Environmental Sciences 2023;36(12):1123-1135
CountryChina
Language:English
Abstract: OBJECTIVE:This 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.
METHODS:Data 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 ( n ₁ = 4,609) and a validation set ( n ₂ = 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.
RESULTS:Our 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.
CONCLUSION:The 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.