ArticleScientific reports2021
A machine learning-based predictor for the identification of the recurrence of patients with gastric cancer after operation.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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37 citing papers in PubMed.
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- Multimodal Machine Learning for Predicting Post-Surgery Quality of Life in Colorectal Cancer Patients.Journal of imaging · 2024Article
- Explainable machine learning models for early gastric cancer diagnosis.Scientific reports · 2024Article
- Development and validation of an inflammatory biomarkers model to predict gastric cancer prognosis: a multi-center cohort study in China.BMC cancer · 2024Article
- A Cost-Effective Model for Predicting Recurrent Gastric Cancer Using Clinical Features.Diagnostics (Basel, Switzerland) · 2024Article
- Machine learning methods predict recurrence of pN3b gastric cancer after radical resection.Translational cancer research · 2024Article
- Artificial intelligence algorithms for predicting post-operative ileus after laparoscopic surgery.Heliyon · 2024Article
- Robustly federated learning model for identifying high-risk patients with postoperative gastric cancer recurrence.Nature communications · 2024Article
- Early gastric cancer recurrence after endoscopic submucosal dissection: Not to be ignored!World journal of gastrointestinal oncology · 2024Article
- Identification of Fatty Acid Metabolism-Related Subtypes in Gastric Cancer Aided by Machine Learning.Cancer management and research · 2024Article
- Challenges involved in the application of artificial intelligence in gastroenterology: The race is on!World journal of gastroenterology · 2023Review
- Landscape of Innovative Methods for Early Diagnosis of Gastric Cancer: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Prediction of HER2 status via random forest in 3257 Chinese patients with gastric cancer.Clinical and experimental medicine · 2023Article
- A practical recurrence risk model based on Lasso-Cox regression for gastric cancer.Journal of cancer research and clinical oncology · 2023Article
- Application of Machine Learning Based on Structured Medical Data in Gastroenterology.Biomimetics (Basel, Switzerland) · 2023Review
- Synergistic markers based on inter-covariate association estimate treatment option with lower propensity to covariates.Annals of translational medicine · 2023Article
- Automated machine learning (AutoML) can predict 90-day mortality after gastrectomy for cancer.Scientific reports · 2023Article
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7 authors.
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Abstract
To explore the predictive performance of machine learning on the recurrence of patients with gastric cancer after the operation. The available data is divided into two parts. In particular, the first part is used as a training set (such as 80% of the original data), and the second part is used as a test set (the remaining 20% of the data). And we use fivefold cross-validation. The weight of recurrence factors shows the top four factors are BMI, Operation time, WGT and age in order. In training group:among the 5 machine learning models, the accuracy of gbm was 0.891, followed by gbm algorithm was 0.876; The AUC values of the five machine learning algorithms are from high to low as forest (0.962), gbm (0.922), GradientBoosting (0.898), DecisionTree (0.790) and Logistic (0.748). And the precision of the forest is the highest 0.957, followed by the GradientBoosting algorithm (0.878). At the same time, in the test group is as follows: the highest accuracy of Logistic was 0.801, followed by forest algorithm and gbm; the AUC values of the five algorithms are forest (0.795), GradientBoosting (0.774), DecisionTree (0.773), Logistic (0.771) and gbm (0.771), from high to low. Among the five machine learning algorithms, the highest precision rate of Logistic is 1.000, followed by the gbm (0.487). Machine learning can predict the recurrence of gastric cancer patients after an operation. Besides, the first four factors affecting postoperative recurrence of gastric cancer were BMI, Operation time, WGT and age.
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