Introduction: One of the main causes of cancer-related mortality globally is gastric cancer, which is more common in developing nations.  Physicians can make better clinical decisions and plan treatments if they can accurately forecast the survival time of patients with stomach cancer.
Methods: This retrospective study was conducted on data from 384 patients diagnosed with gastric cancer over a 20-year period. To predict survival time, a Support Vector Regression (SVR) model with an RBF kernel was applied. SVR was selected due to its strong capability in modeling complex nonlinear relationships in continuous data. To enhance the interpretability of the results, the LIME algorithm was employed to analyze the influence of individual variables. Model performance was evaluated using the C-index, mean absolute error (MAE), and mean squared error (MSE).
Results: The SVR model achieved a C-index of 0.87, MAE of 45.3 days, and MSE of 56.7. LIME analysis showed that while addiction, family history of gastric cancer, and cause of death had negative effects on survival prediction, factors like combination therapy, adenocarcinoma histology, education level, and age at diagnosis had a substantial beneficial impact.
Conclusion: A dependable and understandable model for forecasting survival time in patients with stomach cancer was made possible by the combination of SVR and LIME.The model's interpretability makes it appropriate for clinical settings, where decision-making procedures require openness and trust