ArticleFrontiers in artificial intelligence2022
Machine learning: A non-invasive prediction method for gastric cancer based on a survey of lifestyle behaviors.
Article in Frontiers in artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Predicting clinical outcomes in Helicobacter pylori-positive patients using supervised learning through the integration of demographic and genomic features.BMC gastroenterology · 2026Article
- A Machine Learning Risk Prediction Model for Gastric Cancer with SHapley Additive exPlanations.Cancer research and treatment · 2025Article
- Predictive models of lymph node metastasis in patients with gastrointestinal stromal tumors based on machine learning algorithms: a SEER-based retrospective study.Journal of gastrointestinal oncology · 2025Article
- Predictive Mortality and Gastric Cancer Risk Using Clinical and Socio-Economic Data: A Nationwide Multicenter Cohort Study.Cancers · 2024Article
- Application of Machine Learning in the Diagnosis of Early Gastric Cancer Using the Kyoto Classification Score and Clinical Features Collected from Medical Consultations.Bioengineering (Basel, Switzerland) · 2024Article
- GastricAITool: A Clinical Decision Support Tool for the Diagnosis and Prognosis of Gastric Cancer.Biomedicines · 2024Article
- Diagnosis to dissection: AI's role in early detection and surgical intervention for gastric cancer.Journal of robotic surgery · 2024Review
- Artificial intelligence: clinical applications and future advancement in gastrointestinal cancers.Frontiers in artificial intelligence · 2024Review
- A Framework to Predict Gastric Cancer Based on Tongue Features and Deep Learning.Micromachines · 2022Article
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Authors and funding
6 authors.
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Abstract
Gastric cancer remains an enormous threat to human health. It is extremely significant to make a clear diagnosis and timely treatment of gastrointestinal tumors. The traditional diagnosis method (endoscope, surgery, and pathological tissue extraction) of gastric cancer is usually invasive, expensive, and time-consuming. The machine learning method is fast and low-cost, which breaks through the limitations of the traditional methods as we can apply the machine learning method to diagnose gastric cancer. This work aims to construct a cheap, non-invasive, rapid, and high-precision gastric cancer diagnostic model using personal behavioral lifestyles and non-invasive characteristics. A retrospective study was implemented on 3,630 participants. The developed models (extreme gradient boosting, decision tree, random forest, and logistic regression) were evaluated by cross-validation and the generalization ability in our test set. We found that the model developed using fingerprints based on the extreme gradient boosting (XGBoost) algorithm produced better results compared with the other models. The overall accuracy of which test set was 85.7%, AUC was 89.6%, sensitivity 78.7%, specificity 76.9%, and positive predictive values 73.8%, verifying that the proposed model has significant medical value and good application prospects.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.