ArticleScientific reports2024
Explainable machine learning models for early gastric cancer diagnosis.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed.
- Artificial Intelligence in Gastric Cancer: Diagnostic, Prognostic, and Predictive Developments, Evidence Maturity, and Translational Challenges.Cancer reports (Hoboken, N.J.) · 2026Review
- Longitudinal prediction of BMI using explainable AI: integrating polygenic scores, maternal, early-life and familial factors.International journal of obesity (2005) · 2026Observational
- Development of a machine learning-based model for predicting postoperative survival in gastric cancer.World journal of gastrointestinal surgery · 2026Article
- Machine Learning-Based Diagnostic Models for Early Gastric Cancer Using Clinical Laboratory Indicators.International journal of general medicine · 2026Article
- Development and clinical application of a postoperative complication prognosis prediction model for gastric cancer patients based on automated machine learning with body fat rate.Frontiers in oncology · 2026Article
- Current Role of Artificial Intelligence in the Management of Gastric Cancer.Biomedicines · 2025Review
- WHFDL: an explainable method based on World Hyper-heuristic and Fuzzy Deep Learning approaches for gastric cancer detection using metabolomics data.BioData mining · 2025Article
- Deciphering microbial and metabolic influences in gastrointestinal diseases-unveiling their roles in gastric cancer, colorectal cancer, and inflammatory bowel disease.Journal of translational medicine · 2025Article
- Integrating single-cell RNA sequencing, WGCNA, and machine learning to identify key biomarkers in hepatocellular carcinoma.Scientific reports · 2025Article
- Analysis of cultivated land degradation in southern China: diagnostics, drivers, and restoration solutions.Frontiers in plant science · 2025Article
- A Study of the Effectiveness of Apoptosis Biomarkers in the Diagnosis of Gastric Cancer in Kazakhstan: A Review of Systematic Evidence.Medical journal of the Islamic Republic of Iran · 2025Review
- Article
- Predictive Mortality and Gastric Cancer Risk Using Clinical and Socio-Economic Data: A Nationwide Multicenter Cohort Study.Cancers · 2024Article
- The Role of Neural Network Analysis in Identifying Predictors of Gastric Cancer.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2024Article
- Temporal and spatial trends in gastric cancer burden in the USA from 1990 to 2021: findings from the global burden of disease study 2021.Frontiers in oncology · 2024Article
- Risk Prediction Models for Gastric Cancer: A Scoping Review.Journal of multidisciplinary healthcare · 2024Article
- Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Gastric cancer remains a significant global health concern, with a notably high incidence in East Asia. This paper explores the potential of explainable machine learning models in enhancing the early diagnosis of gastric cancer. Through comprehensive evaluations, various machine learning models, including WeightedEnsemble, CatBoost, and RandomForest, demonstrated high potential in accurately diagnosing early gastric cancer. The study emphasizes the importance of model explainability in medical diagnostics, showing how transparent, explainable models can increase trust and clinical acceptance, thereby improving diagnostic accuracy and patient outcomes. This research not only highlights key biomarkers and clinical features critical for early detection but also presents a versatile approach that could be applied to other medical diagnostics, promoting broader adoption of machine learning in clinical settings.
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Registered trials
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.