Machine learning: A non-invasive prediction method for gastric cancer based on a survey of lifestyle behaviors - Jiang, Siqing; Gao, Haojun; He, Jiajin; Shi, Jiaqi; Tong, Yuling; Wu, Jian (Frontiers in Artificial Intelligence, 2022)
Gastric cancer remain a enormous threat to human health. It is extremely significant to make a clear diagnosis and timely treatment of endocrine 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 3630 participants. The developed models (Extreme Gradient Boosting, Decision Tree, Random Forest, and Logistics 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 XGBoost (Extreme Gradient Boosting) algorithm produced better results than 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.