ArticleBMC medical informatics and decision making2018
Application of data mining methods to improve screening for the risk of early gastric cancer.
Article in BMC medical informatics and decision making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed, 39 citations in OpenAlex.
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- Developing a digital interactive tool to enhance cervical cancer awareness and health literacy in Croatia: a community case study.Frontiers in public health · 2026Article
- A Machine Learning Risk Prediction Model for Gastric Cancer with SHapley Additive exPlanations.Cancer research and treatment · 2025Article
- Landscape of four different stages of human gastric cancer revealed by single-cell sequencing.World journal of gastrointestinal oncology · 2025Article
- A Neoteric Feature Extraction Technique to Predict the Survival of Gastric Cancer Patients.Diagnostics (Basel, Switzerland) · 2024Article
- Application of Data Mining Technology in the Screening for Gallbladder Stones: A Cross-Sectional Retrospective Study of Chinese Adults.Yonsei medical journal · 2024Article
- Mapping Potential Malaria Vector Larval Habitats for Larval Source Management in Western Kenya: Introduction to Multimodel Ensembling Approaches.The American journal of tropical medicine and hygiene · 2024Article
- Global, regional, and national trends in gastric cancer burden: 1990-2021 and projections to 2040.Frontiers in oncology · 2024Article
- Interpretable machine learning for predicting the response duration to Sintilimab plus chemotherapy in patients with advanced gastric or gastroesophageal junction cancer.Frontiers in immunology · 2024Article
- Establishing machine learning models to predict the early risk of gastric cancer based on lifestyle factors.BMC gastroenterology · 2023Article
- Identification of high-risk factors associated with mortality at 1-, 3-, and 5-year intervals in gastric cancer patients undergoing radical surgery and immunotherapy: an 8-year multicenter retrospective analysis.Frontiers in cellular and infection microbiology · 2023Article
- Review
- Using data mining technology to predict medication-taking behaviour in women with breast cancer: A retrospective study.Nursing open · 2022Article
- Evaluation of CSTB and DMBT1 expression in saliva of gastric cancer patients and controls.BMC cancer · 2022Article
- High-risk population's knowledge of risk factors and warning symptoms and their intention toward gastric cancer screening in Southeastern China.Frontiers in public health · 2022Article
- Preferences of first-degree relatives of gastric cancer patients for gastric cancer screening: a discrete choice experiment.BMC cancer · 2021Article
- Status quo and future prospects of artificial neural network from the perspective of gastroenterologists.World journal of gastroenterology · 2021Review
- Preoperative imaging evaluation of the absolute indication criteria for endoscopic submucosal dissection in early gastric cancer patients.Wideochirurgia i inne techniki maloinwazyjne = Videosurgery and other miniinvasive techniques · 2021Article
- Artificial intelligence in gastric cancer: a translational narrative review.Annals of translational medicine · 2021Review
- Different Data Mining Approaches Based Medical Text Data.Journal of healthcare engineering · 2021Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough gastric cancer is a malignancy with high morbidity and mortality in China, the survival rate of patients with early gastric cancer (EGC) is high after surgical resection. To strengthen diagnosing and screening is the key to improve the survival and life quality of patients with EGC. This study applied data mining methods to improve screening for the risk of EGC on the basis of noninvasive factors, and displayed important influence factors for the risk of EGC.
methodsThe dataset was derived from a project of the First Hospital Affiliated Guangdong Pharmaceutical University. A series of questionnaire surveys, serological examinations and endoscopy plus pathology biopsy were conducted in 618 patients with gastric diseases. Their risk of EGC was categorized into low and high risk of EGC by the results of endoscopy plus pathology biopsy. The synthetic minority oversampling technique (SMOTE) was used to solve imbalance categories of the risk of EGC. Four classification models of the risk of EGC was established, including logistic regression (LR) and three data mining algorithms.
resultsThe three data mining models had higher accuracy than the LR model. Gain curves of the three data mining models were convexes more closer to ideal curves by contrast with that of the LR model. AUC of the three data mining models were larger than that of the LR model as well. The three data mining models predicted the risk of EGC more effectively in comparison with the LR model. Moreover, this study found 16 important influence factors for the risk of EGC, such as occupations, helicobacter pylori infection, drinking hot water and so on.
conclusionsThe three data mining models have optimal predictive behaviors over the LR model, therefore can effectively evaluate the risk of EGC and assist clinicians in improving the diagnosis and screening of EGC. Sixteen important influence factors for the risk of EGC were illustrated, which may helpfully assess gastric carcinogenesis, and remind to early prevention and early detection of gastric cancer. This study may also be conducive to clinical researchers in selecting and conducting the optimal predictive models.
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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.