Risk Prediction of Barrett's Esophagus in a Taiwanese Health Examination Center Based on Regression Models

Abstract

Determining the target population for the screening of Barrett's esophagus (BE), a precancerous condition of esophageal adenocarcinoma, remains a challenge in Asia. The aim of our study was to develop risk prediction models for BE using logistic regression (LR) and artificial neural network (ANN) methods. Their predictive performances were compared. We retrospectively analyzed 9646 adults aged ≥20 years undergoing upper gastrointestinal endoscopy at a health examinations center in Taiwan. Evaluated by using 10-fold cross-validation, both models exhibited good discriminative power, with comparable area under curve (AUC) for the LR and ANN models (Both AUC were 0.702). Our risk prediction models for BE were developed from individuals with or without clinical indications of upper gastrointestinal endoscopy. The models have the potential to serve as a practical tool for identifying high-risk individuals of BE among the general population for endoscopic screening.

Keywords: Barrett’s esophagus; Taiwan; computer; logistic models; neural networks.

Figure 1

The architecture of the proposed artificial neural network model.

Figure 2

The receiver operating characteristic (ROC) curves for the logistic regression (LR) and artificial neural network (ANN) models. The green lines show the mean ROC curves, and the gray areas represent the performance within two SD around the mean ROC. (a) The ROC curve of LR model (AUC = 0.702, SD = 0.040); (b) The ROC curve of ANN model. (AUC = 0.702, SD = 0.035). AUC: area under cure; SD: standard deviation.