Xiao Xingjian, Yi Xiaohan, Shi Zumin, Ge Zongyuan, Song Hualing, Zhao Hailei, Liang Tiantian, Yang Xinming, Liu Suxian, Sun Bo, Xu Xianglong
School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Human Nutrition Department, College of Health Sciences, QU Health, Qatar University, Doha, Qatar.
Digit Health. 2025 May 21;11:20552076251336951. doi: 10.1177/20552076251336951. eCollection 2025 Jan-Dec.
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.
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.
We used baseline data from the Chinese Longitudinal Healthy Longevity Survey in 2011 and 2014, with a follow-up endpoint of 2018. We employed nine machine learning algorithms to construct predictive models for gastric ulcers over the next seven years (2011-2018, with 1482 samples) and the next three years (2014-2018, with 2659 samples). Additionally, we utilized cross-sectional data from 2018 (with 13,775 samples) to construct a predictive model for current gastric ulcers.
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.
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.
随着中国人口持续老龄化，胃溃疡作为一种常见的与营养和饮食相关的疾病，在老年人中的患病率正在上升。胃溃疡给中国带来了重大的公共卫生挑战，但准确预测胃溃疡的研究有限。
我们的研究旨在运用机器学习算法预测胃溃疡的发生，并开发一个在线工具，用于评估老年人当前及未来患胃溃疡的风险，同时确定重要的预测因素。
我们使用了2011年和2014年中国健康与养老追踪调查的基线数据，随访终点为2018年。我们运用九种机器学习算法构建了未来七年（2011 - 2018年，1482个样本）和未来三年（2014 - 2018年，2659个样本）胃溃疡的预测模型。此外，我们利用2018年的横断面数据（13775个样本）构建了当前胃溃疡的预测模型。
诸如人口统计学、行为、营养和体格检查等非侵入性预测因素被用于预测当前及未来胃溃疡的发生。在我们的研究中，支持向量机（SVM）、随机森林（RF）和轻量级梯度提升机（LGBM）在预测未来七年胃溃疡时准确率达到0.97；逻辑回归、自适应提升、SVM、RF、梯度提升机、LGBM和K近邻算法在三年预测中达到0.98；SVM、极端梯度提升、RF和LGBM在当前胃溃疡预测中达到0.95。
我们开发了MyGutRisk，基于最优机器学习模型，利用饮食和生活方式等非侵入性因素相对准确地预测老年人患胃溃疡的风险。它支持通过公共链接进行自我评估以及在社区卫生环境中进行临床筛查，以指导预防措施。然而，作为一个原型，它需要进一步验证，以确保在不同人群和实际应用中的准确性和通用性。
