## 4 Discussion  
The prediction of weight loss following bariatric surgery remains a challenging task. A plethora of studies have sought to formulate models for the prediction of weight loss post-surgery. However, retrospective validation of these models has revealed that, while many demonstrated a statistically significant correlation with post-surgery weight, even the most accurate models could only explain 24% of the variation in weight loss [15]. Furthermore, the majority of models tended to overestimate outcomes. While some studies have employed machine learning techniques to identify variables influencing post-surgical weight loss [16], these have frequently been constrained by their reliance on simplistic factors such as height, weight, diabetes status, and diabetes duration. This study presents the first endeavor to formulate a deep learning model that utilizes both imaging and non-imaging data to predict outcomes following sleeve gastrectomy. The model incorporates a comprehensive set of variables, encompassing not only basic characteristics such as height and weight, but also metrics of insulin resistance, blood pressure, and computed tomography (CT) imaging data. The integration of these diverse variables has yielded a novel perspective on predicting weight loss after bariatric surgery. The findings of this study demonstrated that the model achieved a higher level of accuracy in predicting post-surgical weight loss compared to a previous study on a similar topic but with imaging data only [6] (88% vs. 81%). This accuracy gap remained intact for another study, which developed an artificial neural network on non-image data only with 17 variables [10] (88% vs. 70%). Of particular significance was the identification of insulin resistance and secretion markers (HOMA-IR, HOMA2-B, and insulin) as critical predictors of weight loss outcomes. Furthermore, this study identified that the most significant CT cross-sectional region associated with weight change prediction was the diaphragm-level section of the abdomen. The findings of this study indicate that insulin resistance and secretion-related indicators--HOMA2-B, HOMA2-IR, and insulin--play pivotal roles in predicting post \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Variable** & **WD** & & **RF** & \\ \hline  & \(VI\) & _Rank_ & \(VI\) & _Rank_ \\ HOMA2-B & 0.1255 & 4 & 0.1398 & 1 \\ Insulin & 0.1275 & 2 & 0.0770 & 2 \\ Uric Acid & 0.0020 & 8 & 0.0675 & 3 \\ Vitamin B12 & 0.0387 & 5 & 0.0557 & 4 \\ HOMA2-IR & 0.1289 & 1 & 0.0473 & 5 \\ SMM & 0.0000 & 10 & 0.0453 & 6 \\ Glucose & 0.0059 & 6 & 0.0436 & 7 \\ ALT & 0.0054 & 7 & 0.0435 & 8 \\ HOMA2-S & 0.0010 & 9 & 0.0428 & 9 \\ Total Cholesterol & 0.1270 & 3 & 0.0364 & 10 \\ \hline \hline \end{tabular} Note: WD, wide and deep; RF, random forest; VI, variable importance; SMM, skeletal muscle mass.  
\end{table} 
Table 3: Permutation importance. Legend: The importance of a predictor was calculated as the performance gap between the model without its permutation and the model with its permutation (“variable permutation importance”). For example, the importance of HOMA2-B, 0.1255, indicates a performance difference between wide and deep learning without its permutation and wide and deep learning with its permutation. The permutation importance rankings of five predictors, HOMA-B, insulin, vitamin B12, fast plasma glucose, alanine transaminase, and HOMA2-S, were similar in WD and RF, a popular machine learning model. Here, the terms WD and RF denote wide and deep learning and random forest, respectively. surgery weight loss. Insulin resistance and the decline in beta cell function are central to the pathophysiology of type 2 diabetes mellitus (T2 DM). The Homeostasis Model Assessment 2 (HOMA2) is a method that estimates insulin resistance (HOMA2-IR) and \(\beta\)-cell secretion capacity (HOMA2-B) using fasting plasma glucose (FPG) and fasting C-peptide levels. Given that HOMA2-IR reflects insulin resistance, it aligns with the influence of insulin levels on weight loss observed in our model.