Research on Predictive Auxiliary Diagnosis Method for Gastric Cancer Based on Non-Invasive Indicator Detection

作者全名："Zhang, Xia; Zhang, Mao; Wei, Gang; Wang, Jia"

作者地址："[Zhang, Xia; Zhang, Mao; Wei, Gang] Chongqing Univ Sci & Technol, Sch Elect Engn, Chongqing 401331, Peoples R China; [Wang, Jia] Chongqing Med Univ, Coll Med Informat, Chongqing 400016, Peoples R China"

通信作者："Wang, J (通讯作者)，Chongqing Med Univ, Coll Med Informat, Chongqing 400016, Peoples R China."

来源：APPLIED SCIENCES-BASEL

ESI学科分类：ENGINEERING

WOS号：WOS:001306852600001

JCR分区：Q2

影响因子：2.7

年份：2024

卷号：14

期号：16

开始页：　

结束页：　

文献类型：Article

关键词：chronic atrophic gastritis; non-invasive; Random Forest algorithm; influencing factors; prediction model

摘要："Chronic atrophic gastritis is a serious health issue beyond the stomach health problems that affect normal life. This study aimed to explore the influencing factors related to chronic atrophic gastritis (CAG) using non-invasive indicators and establish an optimal prediction model to aid in the clinical diagnosis of CAG. Electronic medical record data from 20,615 patients with CAG were analyzed, including routine blood tests, liver function tests, and coagulation tests. The logistic regression algorithm revealed that age, hematocrit, and platelet distribution width were significant influences suggesting chronic atrophic gastritis in the Chongqing population (p < 0.05), with an area under the curve (AUC) of 0.879. The predictive model constructed based on the Random Forest algorithm exhibited an accuracy of 83.15%, precision of 97.38%, recall of 77.36%, and an F1-score of 70.86%, outperforming the models constructed using XGBoost, KNN, and SVC algorithms in a comprehensive comparison. The prediction model derived from this study serves as a valuable tool for future studies and can aid in the prediction and screening of chronic atrophic gastritis."

基金机构：Science and Technology Research Program of Chongqing Municipal Education Commission [KJQN202100434]; Research foundation of Chongqing University of Science and Technology [182201038]; Intelligent Medicine Program of Chongqing Medical University [ZHYX202120]

基金资助正文："This research was funded by the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJQN202100434), the research foundation of Chongqing University of Science and Technology (Grant No. 182201038), and the Intelligent Medicine Program of Chongqing Medical University (Grant No. ZHYX202120)."