Early screening for gastric cancer using machine learning techniques

Abstract

The feasibility of using machine-learning techniques to screen dyspeptic patients for those at high risk of gastric cancer was demonstrated in this study. Data on 1401 dyspeptic patients over the age of 40, consisted of 85 epidemiological and clinical variables and a gold-standard diagnosis, made by upper gastrointestinal endoscopy. The diagnoses were grouped into two classes — those at high risk of having (or developing) gastric cancer and those at low risk. A machine-learning approach was used to generate a cross-validated sensitivity-specificity curve in order to assess the power of the discrimination between the two groups.