Objective Evaluate the predictive efficacy of six machine learning (ML) algorithms in identifying peritoneal metastasis in gastric cancer (GC) patients.

Methods Data from 809 GC patients (712 non-metastasis, 97 metastasis) were split into training and test sets (80:20). Six ML models—Decision Trees (DT), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Naive Bayes (NB), Random Forest (RF), and Logistic Regression (LR)—were assessed for feature importance and predictive performance.

Results Lymph node positivity, lymph nodes cleaned, invasion depth, lymphatic invasion, and node dissection extent were key predictors. Among inflammatory markers, PLR was significant (p = 0.018), while NLR was not (p = 0.121). RF achieved the highest accuracy (97%), followed by SVM and LR.

Conclusion ML enables robust prediction of peritoneal metastasis, with RF demonstrating the best performance. These findings highlight ML’s role in risk stratification, though multi-center validation is required for clinical application.

The authors have declared no competing interest.

This study did not receive any funding

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