Predicting Peritoneal Metastasis of Gastric Cancer Patients Based on Machine Learning.

ABSTRACT:

Objective

The aim is to explore the prediction effect of 5 machine learning algorithms on peritoneal metastasis of gastric cancer.

Methods

1080 patients with postoperative gastric cancer were divided into a training group and test group according to the ratio of 7:3. The model of peritoneal metastasis was established by using 5 machine learning (gbm(Light Gradient Boosting Machine), GradientBoosting, forest, Logistic and DecisionTree). Python pair was used to analyze the machine learning algorithm. Gbm algorithm is used to show the weight proportion of each variable to the result.

Result

Correlation analysis showed that tumor size and depth of invasion were positively correlated with the recurrence of patients after gastric cancer surgery. The results of the gbm algor

SUBMITTER: Zhou C 

PROVIDER: S-EPMC7791448 | biostudies-literature | 2020 Jan-Dec

REPOSITORIES: biostudies-literature