## 4 Discussion  
In the present study, a mortality prediction model of patients undergoing gastric cancer surgery mainly based on body morphometry, nutritional, and surgical information using CNN was developed. The presented model was constructed using 15 selected features that can be divided into four categories: clinicopathologic (age, sex, height, BMI, and TNM stage), body morphometry (SFA, VFA, SMA/height2, and SMA/BMI), nutritional (hemoglobin, albumin, cholesterol, and NRI), and surgical information (type of operation and type of anastomosis). This AI model based on the abovementioned information  
Figure 6: Deployed web application: user inputs both preoperative and postoperative values for standardization of the time-dependent continuous variables. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Performance** & **All Validation Sets** & **Internal Validation Set** & **External Validation Set** \\  & **(n = 53)** & **(n = 20)** & **(n = 33)** \\ \hline Accuracy & 0.887 & 0.900 & 0.879 \\ F1 score & 0.889 & 0.909 & 0.882 \\ AUC & 0.887 & 0.900 & 0.881 \\ \hline \hline \end{tabular} AUC = area under the receiver-operating characteristic curve.  
\end{table} 
Table 3: Performance of our CNN model on validation sets. presented a substantial performance in predicting mortality (accuracy 0.887, F1 score 0.889, and AUC 0.887 in all validation sets). The AI method is known to be better than the Cox proportional hazard (CPH) model, which is the conventional method to calculate the risk factors for prognosis, due to the fact that AIs can process and analyze large amounts of data and can learn linear as well as nonlinear associations between prognostic clinical characteristics and an individual's risk of death [24]. [PERSON] et al. [25] and [PERSON] et al. [26] showed that the Artificial Neural Network (ANN) is a more powerful tool in determining the significant prognostic variables for gastric cancer patients compared to the CPH model in both accuracy and AUC. Among deep learning methods, as shown in [PERSON] et al. [27] and [PERSON] et al. [28], the CNN is better than other deep learning approaches, including recurrent neural networks (RNNs), autoencoders, and others for predicting diseases based on patient similarity. Considering these findings, we employed a CNN. Compared with other prior studies that developed AI models to predict overall survival in gastric cancer patients treated with gastrectomy (accuracy, ranging from 0.826 to 0.891), our CNN model showed analogous accuracy (0.900 in the internal validation set and 0.879 in the external validation set) for predicting overall survival rates [25; 29; 30]. The risk factors used in these AI models differed across studies. For example, [PERSON] et al. developed a prediction model (accuracy 85.3%) using an ANN with risk factors of disease stage, radical surgery, serum CA19-9 level, peritoneal dissemination, and BMI [25]. [PERSON] et al. developed a prediction model (AUC 0.80) using random survival forests with risk factors of age, cT stage, cN stage, WHO performance status, ASA grade, pT/ypT, lymph node metastasis, differentiation grade, resection completeness, and neoadjuvant treatment [24]. These AI models used in prior studies have relied primarily on clinicopathological information, such as pathologic staging, including tumor size, penetration, lymph node metastasis, and distant metastasis, with or without demographic information [13; 14; 15]. The current study, on the other hand, describes the development of an AI model mainly based on body morphometry, nutritional, and surgical information. With respect to clinicopathologic information, only demographic data and the TNM stage were used. The vision behind the development of this AI model using these data was to enable both doctors and patients to enter patient data easily. Generally, pathologic information, such as tumor size, extent of tumor penetration, and number of lymph node metastases, is challenging to obtain for daily practitioners and patients.