###### Abstract  
The objective of this study is to develop a mortality prediction model for patients undergoing gastric cancer surgery based on body morphometry, nutritional, and surgical information. Using a prospectively built gastric surgery registry from the Asan Medical Center (AMC), 621 gastric cancer patients, who were treated with surgery with no recurrence of cancer, were selected for the development of the prediction model. Input features (i.e., body morphometry, nutritional, surgical, and clinicopathologic information) were selected in the collected data based on the XGBoost analysis results and experts' opinions. A convolutional neural network (CNN) framework was developed to predict the mortality of patients undergoing gastric cancer surgery. Internal validation was performed in split datasets of the AMC, whereas external validation was performed in patients in the Ajou University Hospital. Fifteen features were selected for the prediction of survival probability based on the XGBoost analysis results and experts' suggestions. Accuracy, F1 score, and area under the curve of our CNN model were 0.900, 0.909, and 0.900 in the internal validation set and 0.879, 0.882, and 0.881 in the external validation set, respectively. Our developed CNN model was published on a website where anyone could predict mortality using individual patients' data. Our CNN model provides substantially good performance in predicting mortality in patients undergoing surgery for gastric cancer, mainly based on body morphometry, nutritional, and surgical information. Using the web application, clinicians and gastric cancer patients will be able to efficiently manage mortality risk factors.  
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In general, a variety of prognostic factors have been revealed including age, primary tumor extent, lymph node metastasis, surgical methods, comorbidities such as metabolic syndrome, lifestyle choices such as smoking or alcohol consumption, nutritional status, and sarcopenia [5; 6; 7]. In our prior study, nutritional variables and skeletal muscle mass were observed to decrease after gastrectomy, leading to a loss of 8-15% of the initial body weight and 3-5% of the muscle area [8]. Additionally, it is established that surgical information, such as the type of operation (i.e., total gastrectomy vs. distal gastrectomy) and the type of anastomosis (Billroth-1[gastroduodenostomy], Billroth-2[gastrojejunostomy], Roux-en-Y gastrojeunostomy, etc.), influence the body's morphometry and nutrition after gastrectomy [8]. In addition, the prognostic effects of body morphometry and nutrition in patients treated with gastrectomy have been reported in many works [9; 10; 11]. Furthermore, several prior studies have reported that surgical methods can also affect the patient's survival outcome [12]. Based on prior study results, we believe that a mortality prediction model should include body morphometry, nutritional, and surgical information, as well as clinicopathologic information.  
Recently, artificial intelligence (AI) has been applied to the prognosis of gastric cancer patients treated with gastrectomy [13; 14; 15]. The majority of studies used clinicopathologic information, including tumor staging, tumor extent, lymph node metastasis status, and distant metastasis. However, up to now, these AI prediction models studies have not assessed other crucial information, such as body morphometry, nutritional factors, and surgical information.  
Based on the above, our goal was to develop an AI mortality prediction model for patients undergoing gastric cancer surgery based on body morphometry and nutritional factors. We aimed to make the prediction model available online so that clinicians and gastric cancer patients could predict mortality using the information of individual patients.