Predicting postoperative gastric cancer prognosis based on inflammatory factors and machine learning technology

Abstract

OBJECTIVE: There is a strong association between gastric cancer and inflammatory factors. Many studies have shown that machine learning can predict cancer patients' prognosis. However, there has been no study on predicting gastric cancer death based on machine learning using related inflammatory factor variables. METHODS: Six machine learning algorithms are applied to predict total gastric cancer death after surgery. RESULTS: The Gradient Boosting Machine (GBM) algorithm factors accounting for the prognosis weight outcome show that the three most important factors are neutrophil-lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR) and age. The total postoperative death model showed that among patients with gastric cancer from the predictive test group: The highest accuracy was LR (0.759), followed by the GBM algorithm (0.733). For the six algorithms, the AUC values, from high to low, were LR, GBM, GBDT, forest, Tr and Xgbc. Among the six algorithms, Logistic had the highest precision (precision = 0.736), followed by the GBM algorithm (precision = 0.660). Among the six algorithms, GBM had the highest recall rate (recall = 0.667). CONCLUSION: Postoperative mortality from gastric cancer can be predicted based on machine learning.