Predicting early gastric cancer risk using machine learning: A population-based retrospective study
Xing Ke,
Xinyu Cai,
Bingxian Bian
et al.
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 les… Show more
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