Precision Forecasting in Colorectal Oncology: Predicting Six-Month Survival to Optimize Clinical Decisions

Abstract

Colorectal cancer (CRC) has a relatively high five-year survival rate compared to other cancers; however, this rate drops significantly in patients with malignant CRC. One critical factor in palliative care decision-making is the ability to accurately predict patient survival, with the six-month survival period commonly used as a threshold. In this study, we evaluated the performance of five machine learning models-logistic regression, decision tree, random forest, multilayer perceptron, and extreme gradient boosting (XGBoost)-in predicting six-month survival for patients with malignant CRC using a publicly available synthetic dataset containing 11,774 samples and 51 features. The models were trained and validated using five-fold cross-validation, and the synthetic minority oversampling technique (SMOTE) was applied to address class imbalance. Among the models, XGBoost demonstrated the highest performance, achieving 95% accuracy, precision, recall, and F1-score, along with 90% specificity. Feature importance analysis identified smoking status and surgical history as key factors influencing model predictions. These findings highlight the potential of tree-based machine learning models in supporting timely and informed palliative care decisions, while also providing insights into handling data imbalance and optimizing model parameters in survival prediction tasks.