A novel model for predicting fatty liver disease by means of an artificial neural network. (24th August 2020)

• Record Type: Journal Article
• Authors: Chen, Yi-Shu; Chen, Dan; Shen, Chao; Chen, Ming; Jin, Chao-Hui; Xu, Cheng-Fu; Yu, Chao-Hui; Li, You-Ming
• Is Part Of: Gastroenterology report. Volume 9:Number 1(2021)
• Page Start: 31
• Page End: 37
• Publication Date: 2020-08-24
• Subjects: artificial neural network -- diagnostic model -- fatty liver disease -- Fatty Liver Index -- Hepatic Steatosis Index -- uric acid; Gastroenterology -- Periodicals; 616.33
• DOI: 10.1093/gastro/goaa035
• Languages: English
• ISSNs: 2052-0034

Abstract

Abstract: Background: The artificial neural network (ANN) emerged recently as a potent diagnostic tool, especially for complicated systemic diseases. This study aimed to establish a diagnostic model for the recognition of fatty liver disease (FLD) by virtue of the ANN. Methods: A total of 7, 396 pairs of gender- and age-matched subjects who underwent health check-ups at the First Affiliated Hospital, College of Medicine, Zhejiang University (Hangzhou, China) were enrolled to establish the ANN model. Indices available in health check-up reports were utilized as potential input variables. The performance of our model was evaluated through a receiver-operating characteristic (ROC) curve analysis. Other outcome measures included diagnostic accuracy, sensitivity, specificity, Cohen's k coefficient, Brier score, and Hosmer-Lemeshow test. The Fatty Liver Index (FLI) and the Hepatic Steatosis Index (HSI), retrained using our training-group data with its original designated input variables, were used as comparisons in the capability of FLD diagnosis. Results: Eight variables (age, gender, body mass index, alanine aminotransferase, aspartate aminotransferase, uric acid, total triglyceride, and fasting plasma glucose) were eventually adopted as input nodes of the ANN model. By applying a cut-off point of 0.51, the area under ROC curves of our ANN model in predicting FLD in the testing group was 0.908 [95% confidence interval (CI), 0.901–0.915]—significantly higher ( P < 0.05) than that of the FLI model (0.881, 95% CI, 0.872–0.891) and that of the HSI model (0.885; 95% CI, 0.877–0.893). Our ANN model exhibited higher diagnostic accuracy, better concordance with ultrasonography results, and superior capability of calibration than the FLI model and the HSI model. Conclusions: Our ANN system showed good capability in the diagnosis of FLD. It is anticipated that our ANN model will be of both clinical and epidemiological use in the future.