Applying artificial intelligence using routine clinical data for preoperative diagnosis and prognosis evaluation of gastric cancer.

ABSTRACT: The present study employed artificial intelligence (AI) machine learning technology to evaluate the prognosis of gastric cancer using blood collection data, commonly used in clinical practice and subsequently performed a stratification distinct from conventional tumor-node-metastasis (TNM) classification. Experiments were conducted using four machine learning methods, namely, logistic regression (LR), random forest (RF), gradient boosting (GB) and deep neural network (DNN), to classify good or poor post-5-year prognosis based on clinicopathological data and post-5-year relapse occurrence. For each machine learning method, the importance was sorted in descending order (from the most to the least); the top features were used for clustering using the k-medoids method. The prediction accuracy

SUBMITTER: Kuwayama N 

PROVIDER: S-EPMC10579989 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature