Predicting early gastric cancer risk using machine learning: A population-based retrospective study.

ABSTRACT:

Background

Early detection and treatment are crucial for reducing gastrointestinal tumour-related mortality. The diagnostic efficiency of the most commonly used diagnostic markers for gastric cancer (GC) is not very high. A single laboratory test cannot meet the requirements of early screening, and machine learning methods are needed to aid the early diagnosis of GC by combining multiple indicators.

Methods

Based on the XGBoost algorithm, a new model was developed to distinguish between GC and precancerous lesions in newly admitted patients between 2018 and 2023 using multiple laboratory tests. We evaluated the ability of the prediction score derived from this model to predict early GC. In addition, we investigated the efficacy of the model in correctly screening for GC give

SUBMITTER: Ke X 

PROVIDER: S-EPMC10979538 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature