Machine Learning-Based Mortality Prediction Model Using Minimal Features to Assist Decision-Making in End-of-Life Care.

Introduction International guidelines recommend single or hypo-fraction for palliative radiotherapy (PRT). However, due to the inherent difficulty in accurately predicting life expectancy, the customization of end-of-life care for individual patients poses a significant challenge. This study aims to develop a machine learning-based mortality prediction model for PRT tailored to life expectancy. Methods A retrospective analysis encompassed 318 patients who expired after receiving PRT for advanced cancer from March 2013 to July 2023. Various model algorithms employing 22 variables were used to predict mortality within 30 days from initiation of PRT: extra trees, random forest, light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost). We evaluated each model's performance using accuracy, precision, recall, specificity, and F1 score. Results A total of 302 patients were analyzed after excluding those with missing data. The <30-day mortality group had significantly worse Eastern Cooperative Oncology Group (ECOG) status, lower albumin, higher neutrophil‑to‑lymphocyte ratio, and lower lymphocyte count (all p<0.05). No significant multicollinearity was observed. Among all models, LightGBM showed the best performance (accuracy: 0.725, F1-score: 0.720). A minimal variable model (MVM) using the top eight features plus sex achieved comparable performance to the full variable model (FVM), with improved recall and reduced complexity. Conclusion We developed a machine learning-based mortality prediction model for tailoring PRT to life expectancy. The model identified clinically meaningful predictors that reflect patient condition and tumor burden. The MVM demonstrated a comparable performance to the FVM, suggesting its potential utility as an interpretable and practical clinical decision support tool for individualized end-of-life care planning.