Predicting the stage of gastric cancer after gastrectomy based on machine learning algorithms

BackgroundGastric cancer (GC) is the fourth most common cause of cancer death worldwide, with a 5-year survival rate of less than 40%. One of the most important methods for diagnosing stomach cancer is endoscopy, which is quite costly and invasive. The aim of this study was to develop machine learning-based diagnostic prediction models for the stage of GC. ObjectivesTo create a highly accurate predictive model for the stage of GC in patients via a noninvasive method based on machine learning (ML). MethodsIn this study, data from 996 patients with GC after gastrectomy were utilized. The data were split into groups, trained and tested, and a ratio of 8:2 was used to develop different machine learning models. Furthermore, the six different machine learning algorithms used in predicting the stage of GC include decision tree (DT), K nearest neighbor (KNN), logistic regression (LR), naive Bayes (NB), random forest (RF), and support vector machine (SVM) methods. Results: The analysis of the demographic variables revealed statistically significant differences in the PLR and NLR and other parameters between the two groups of patients with stages I and III gastric cancer (P < 0.05). ResultsThe analysis of demographic variables revealed statistically significant differences in the PLR, NLR, and other variables between the two groups of patients with stages I and III gastric cancer, with a significance level of P-value < 0.05. Moreover, these findings suggest that the KNN model in this study is one of the best models for predicting the stage of GC.