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- Title
Research on Predictive Auxiliary Diagnosis Method for Gastric Cancer Based on Non-Invasive Indicator Detection.
- Authors
Zhang, Xia; Zhang, Mao; Wei, Gang; Wang, Jia
- Abstract
Featured Application: The methodology and findings can be used as a basis for further research into other predictive models for various diseases, contributing to the advancement of predictive analytics in healthcare. 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.
- Subjects
RANDOM forest algorithms; ATROPHIC gastritis; ELECTRONIC health records; LIVER function tests; BLOOD testing
- Publication
Applied Sciences (2076-3417), 2024, Vol 14, Issue 16, p6858
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app14166858