Clinical and Behavioral Determinants of Type 2 Diabetes Remission After Bariatric Surgery: An Explainable Machine Learning Approach

Background: Achieving remission of type 2 diabetes mellitus (T2DM) after bariatric surgery represents a critical opportunity to reduce long-term diabetes-related complications, including cardiovascular disease, nephropathy, neuropathy, and retinopathy. However, remission rates vary widely across patients, and identifying modifiable clinical and behavioral determinants remains essential for optimizing integrated metabolic care. Objectives: In the current study, we aimed to (1) classify type 2 diabetes mellitus (T2DM) remission status after bariatric surgery through clinical, anthropometric, and behavioral variables at follow-up; (2) identify the main model-based determinants of remission status and explainable machine learning using the preoperative model for baseline risk stratification with surgical candidates. Methods: We performed a retrospective cross-sectional study on 233 patients with T2DM who had bariatric surgery at a tertiary referral center. We made use of two analytical frameworks: a full-feature approach to identify the current remission status in a cross-sectional manner and a preoperative approach to make a temporal classification of the baseline for the first time. We trained and internally assessed 14 machine learning and deep learning classifiers. We evaluated model interpretability using SHAP. Results: In the full-feature cross-sectional classification, the Bottleneck Network performed best (ROC AUC = 0.889). SHAP data identified percentage weight regain, pre- and post-surgical body mass index, HbA1c, and oral hypoglycemic agent use as the dominant model-associated factors. In the restricted preoperative setting, the Extra Trees model achieved an AUC of 0.707, which is a lower level but still represents good baseline risk stratification performance. Conclusions: The findings indicate that remission status after bariatric surgery is both clinical as well as behavioral, but the post-operative or contemporaneously assessed variables should be looked at as classification (as opposed to prediction) models. The preoperative model may be able to be used in risk stratification, but clinical validation and prospective evaluation should be made prior to clinical implementation.