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

Po-Hsiang Lin, Jer-Guang Hsieh, Hsien-Chung Yu, Jyh-Horng Jeng, Chiao-Lin Hsu, Chien-Hua Chen and Pin-Chieh Wu
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Po-Hsiang Lin: Department of Emergency Medicine, Kaohsiung Veterans General Hospital, Kaohsiung 813, Taiwan
Jer-Guang Hsieh: Department of Electrical Engineering, I-Shou University, Kaohsiung 840, Taiwan
Hsien-Chung Yu: Division of Gastroenterology and Hepatology, Department of Internal Medicine, Kaohsiung Veterans General Hospital, Kaohsiung 813, Taiwan
Jyh-Horng Jeng: Department of Information Engineering, I-Shou University, Kaohsiung 840, Taiwan
Chiao-Lin Hsu: Health Management Center, Kaohsiung Veterans General Hospital, 386, Ta-Chung 1st Road, Kaohsiung 813, Taiwan
Chien-Hua Chen: Department of Electrical Engineering, I-Shou University, Kaohsiung 840, Taiwan
Pin-Chieh Wu: Health Management Center, Kaohsiung Veterans General Hospital, 386, Ta-Chung 1st Road, Kaohsiung 813, Taiwan

IJERPH, 2021, vol. 18, issue 10, 1-10

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; logistic models; neural networks; computer; Taiwan (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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