Machine learning predicts cancer-associated venous thromboembolism using clinically available variables in gastric cancer patients. Issue 1 (January 2023)

• Record Type: Journal Article
• Authors: Xu, Qianjie; Lei, Haike; Li, Xiaosheng; Li, Fang; Shi, Hao; Wang, Guixue; Sun, Anlong; Wang, Ying; Peng, Bin
• Is Part Of: Heliyon. Volume 9:Issue 1(2023)
• Publication Date: 2023-01
• Subjects: Gastric cancer -- Venous thromboembolism -- Prediction model -- Machine learning; Research -- Periodicals; Medical sciences -- Periodicals; Natural history -- Periodicals; Social sciences -- Periodicals; Earth sciences -- Periodicals; Physical sciences -- Periodicals; 507.2
• DOI: 10.1016/j.heliyon.2022.e12681
• Languages: English
• ISSNs: 2405-8440

Abstract

Abstract: Stomach cancer (GC) has one of the highest rates of thrombosis among cancers and can lead to considerable morbidity, mortality, and additional costs. However, to date, there is no suitable venous thromboembolism (VTE) prediction model for gastric cancer patients to predict risk. Therefore, there is an urgent need to establish a clinical prediction model for VTE in gastric cancer patients. We collected data on 3092 patients between January 1, 2018 and December 31, 2021. And after feature selection, 11 variables are reserved as predictors to build the model. Five machine learning (ML) algorithms are used to build different VTE predictive models. The accuracy, sensitivity, specificity, and AUC of these five models were compared with traditional logistic regression (LR) to recommend the best VTE prediction model. RF and XGB models have selected the essential characters in the model: Clinical stage, Blood Transfusion History, D-Dimer, AGE, and FDP. The model has an AUC of 0.825, an accuracy of 0.799, a sensitivity of 0.710, and a specificity of 0.802 in the validation set. The model has good performance and high application value in clinical practice, and can identify high-risk groups of gastric cancer patients and prevent venous thromboembolism.