Gastroenterology Research and Practice / 2019 / Article / Tab 3 / Research Article
Using Machine Learning to Predict Progression in the Gastric Precancerous Process in a Population from a Developing Country Who Underwent a Gastroscopy for Dyspeptic Symptoms Table 3 Results of linear regression for the predictors identified by best subset linear regression with the leave-one-out method.
Outcome Predictors Regression coefficients (95% confidence intervals) Overall progression (general model) Complete intestinal metaplasia 0.534 (0.425, 0.644) Incomplete intestinal metaplasia 0.316 (0.187, 0.444) Histological diagnosis at baseline less advanced than atrophic gastritis -0.313 (-0.368, -0.258) Depth of corpus inflammation at baseline -0.152 (-0.265, -0.039) Average density of polymorphonuclear cells in the antrum at baseline 0.037 (-0.029, 0.103) Alcohol intake at baseline -0.189 (-0.201, 0.022) Overall progression (location-specific model) Complete intestinal metaplasia at baseline 0.492 (0.382, 0.602) Incomplete intestinal metaplasia at baseline 0.345 (0.223, 0.467) Histological diagnosis at baseline less advanced than atrophic gastritis -0.296 (-0.351, -0.241) Depth of corpus inflammation at baseline -0.150 (-0.264, -0.037) Density of H. pylori infection in the corpus and the antrum at baseline 0.122 (0.030, 0.214) Intake of fried fava beans per week at baseline 0.064 (0.0004, 0.128)