Explainable machine learning for predicting lung metastasis of colorectal cancer.

ABSTRACT: Patients with lung metastasis of colorectal cancer typically have a poor prognosis. Therefore, establishing an effective screening and diagnosis model is paramount. Our study seeks to construct and verify a predictive model utilizing machine learning (ML) that can evaluate the risk of lung metastasis with newly diagnosed colorectal cancer (CRC) using Shapley Additive exPlanations (SHAP). Using the Surveillance, Epidemiology, and End Results database, 39,674 were extracted for model development, all of whom had been pathologically diagnosed with CRC. The data spans from 2010 to 2015. Our study has constructed seven ML algorithms based on the data mentioned above, including Random Forest (RF), Decision Tree, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, eXtreme Gradient Boosting,

SUBMITTER: Guo Z 

PROVIDER: S-EPMC12009389 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature