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TRV-2026-1128Certified recordPeer-reviewed

Development of an Explainable Machine Learning Model for Predicting Reflux Esophagitis Among Candidates for Metabolic Bariatric Surgery

Background Reflux esophagitis (RE) is a common gastroesophageal disorder among candidates for metabolic bariatric surgery (MBS). This study aimed to develop and internally validate an explainable machine learning (ML) model for predicting preoperative RE risk among patients undergoing MBS. Methods This retrospective study included 1,068 MBS candidates who were randomly divided into training and internal test cohorts (7:3). Feature selection was performed using correlation analysis, multicollinearity assessment,…

Health · G Space — documented gain · certified 2026-09-18 · v1 · article view · machine-readable

Current reading — gain

An explainable XGBoost model using six routine preoperative variables enabled individualized reflux esophagitis risk prediction for metabolic bariatric surgery candidates with AUC 0.881 training and 0.825 internal test.

What this doesn’t fix

Model was only internally validated in a single retrospective cohort; external validation is still required to assess robustness and generalizability.

Evidence

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Truvace Impact Record TRV-2026-1128, v1: “Development of an Explainable Machine Learning Model for Predicting Reflux Esophagitis Among Candidates for Metabolic Bariatric Surgery.” Truvace, 2026-09-18. /record/TRV-2026-1128 (accessed at citation time). sha256 c74a77a4c82b3af4…

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