A web-based tool for predicting gastric ulcers in Chinese elderly adults based on machine learning algorithms and noninvasive predictors - national cross-sectional and cohort study

Aug 5, 2026

As China’s population ages, gastric ulcers—a nutrition- and diet-related disorder—are increasingly prevalent, yet accurate prediction remains limited. This national cross-sectional and cohort study develops machine-learning models using baseline data from 2011/2014 with follow-up through 2018 and cross-sectional 2018 data. It builds an online risk-assessment tool (MyGutRisk) using noninvasive demographic, behavioral, nutritional, and physical examination predictors to support current and future risk estimation, and self-assessment and community screening, while requiring further validation.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Abstract

Background

Objective

Method

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Introduction

Original research article A web-based tool for predicting gastric ulcers in Chinese elderly adults based on machine learning algorithms and noninvasive predictors: A national cross-sectional and cohort study DIGITAL HEALTH Volume 11: 1–13 © The Author(s) 2025 Article reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/20552076251336951](DOI: 10.1177/20552076251336951)[ ](DOI: 10.1177/20552076251336951)[journals.sagepub.com/home/dhj](journals.sagepub.com/home/dhj) Xingjian Xiao 1 , Xiaohan Yi 1, Zumin Shi2, Zongyuan Ge3, Hualing Song 1, Hailei Zhao 1, Tiantian Liang 1, Xinming Yang 1, Suxian Liu4, Bo Sun4 and Xianglong Xu 1,5 Abstract Background: As the Chinese population continues to age, the prevalence of gastric ulcers, a common nutrition and diet-related disorder, is rising among the elderly. Gastric ulcers pose a signiﬁcant public health challenge in China, yet there is limited research to predict gastric ulcers accurately. Objective: Our study aims to employ machine learning algorithms to predict the occurrence of gastric ulcers and develop an online tool to assess the risk of gastric ulcers for elderly individuals, both currently and in the future, while identifying important predictors. Method: We used baseline data from the Chinese Longitudinal Healthy Longevity Survey in 2011 and 2014, with a follow-up endpoint of2018. We employed nine machine learning algorithms to construct predictive models forgastric ulcers overthe next sevenyears(2011–2018, with1482samples)andthe next three years(2014–2018, with2659samples). Additionally, we utilized cross-sectional data from 2018 (with 13,775 samples) to construct a predictive model for current gastric ulcers. Results: Noninvasive predictors such as demographic, behavioral, nutritional, and physical examination factors were utilized to predict the current and future occurrence of gastric ulcers. In our study, Support Vector Machine (SVM), Random Forest (RF), and Light Gradient Boosting Machine (LGBM) achieved an accuracy of 0.97 for predicting gastric ulcers over seven years; Logistic Regression, Adaptive Boosting, SVM, RF, Gradient Boosting Machine, LGBM, and K-Nearest Neighbors reached 0.98 for three-year predictions; and SVM, Extreme Gradient Boosting, RF, and LGBM attained 0.95 for current gastric ulcer prediction. Conclusions: We developed MyGutRisk, built on optimal machine learning models, relatively accurately predicts gastric ulcer risk in elderly adults using noninvasive factors like diet and lifestyle. It supports self-assessment via a public link and clinical screening in community health settings to guide preventive measures. However, as a prototype, it requires further validation to ensure accuracy and generalizability across diverse populations and real-world applications. Keywords China, gastric ulcer, elderly, ageing, machine learning, risk prediction model, web-based tool Received: 8 October 2024; accepted: 7 April 2025 1 School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China 2Human Nutrition Department, College of Health Sciences, QU Health, Qatar University, Doha, Qatar 3Monashe-Research Center, Faculty of Engineering, Airdoc Research, Nvidia AI Technology Research Center, Monash University, Melbourne,Victoria,Australia 4Endoscopy Center, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China 5School of Translational Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia Corresponding authors: Xianglong Xu, School of Public Health, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong New Area, Shanghai City 201203, China. E-mail: xianglongxu@shutcm.edu.cn Bo Sun, Endoscopy Center, Longhua hospital, Shanghai University of Traditional Chinese Medicine, 725 Wanping South Road, Xuhui District, Sh

FAQs

What problem does the study address?

It addresses the lack of accurate ways to predict gastric ulcers among Chinese elderly adults as the condition becomes more common with population aging.

Which data and modeling approach were used?

Baseline data came from the Chinese Longitudinal Healthy Longevity Survey (2011 and 2014) with follow-up to 2018, plus cross-sectional 2018 data. Nine machine learning algorithms were used to build models for 7-year, 3-year, and current ulcer prediction.

How is the resulting tool intended to be used?

MyGutRisk provides online risk assessment for self-evaluation via a public link and can support clinical screening in community health settings. Further validation is needed to confirm accuracy and generalizability.

Aug 5, 2026

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