Application of machine learning in the diagnosis of gastric cancer based on noninvasive characteristics.

ABSTRACT:

Background

The diagnosis of gastric cancer mainly relies on endoscopy, which is invasive and costly. The aim of this study is to develop a predictive model for the diagnosis of gastric cancer based on noninvasive characteristics.

Aims

To construct a predictive model for the diagnosis of gastric cancer with high accuracy based on noninvasive characteristics.

Methods

A retrospective study of 709 patients at Zhejiang Provincial People's Hospital was conducted. Variables of age, gender, blood cell count, liver function, kidney function, blood lipids, tumor markers and pathological results were analyzed. We used gradient boosting decision tree (GBDT), a type of machine learning method, to construct a predictive model for the diagnosis of gastric cancer and evaluate the acc

SUBMITTER: Zhu SL 

PROVIDER: S-EPMC7775073 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature