Machine learning–based prediction of mortality in lung cancer: Application of severity-adjustment method

Background Lung cancer is a leading cause of cancer-related deaths globally, affecting 2.2 million people and causing approximately 1.8 million deaths annually. The 5-year survival rate remains low due to risk factors such as aging, smoking, and air pollution, which also impose significant financial burdens on healthcare systems. Early mortality prediction can improve patient outcomes and reduce costs. Objective This study aimed to develop mortality prediction models for lung cancer patients using machine learning techniques by comparing severity adjustment tools—Charlson Comorbidity Index (CCI), Age-adjusted Charlson Comorbidity Index (ACCI), and Elixhauser Comorbidity Index (ECI). Methods Data from the National Hospital Discharge Injury Survey (2006–2018) were analyzed for 24,472 lung cancer patients diagnosed with ICD-10 codes C33–C34. Machine learning methods, including logistic regression, decision trees, random forests, XGBoost, support vector machines, and neural networks, were applied. Models were evaluated using area under the curve (AUC), sensitivity, specificity, accuracy, and F1 score. Results Random forests performed best in the training set, while XGBoost and neural networks showed superior performance in the test set. Key predictors of mortality included admission route, length of stay, and surgery status. Conclusions XGBoost and neural network models demonstrated strong performance in predicting mortality among lung cancer patients. This study represents one of the first comparative analyses to evaluate machine learning–based mortality prediction models incorporating multiple comorbidity severity indices (CCI, ACCI, and ECI), providing evidence to support the selection of optimal severity adjustment tools and machine learning algorithms. Implementing these models, in combination with primary care and remote monitoring strategies after hospital discharge, may help reduce emergency room visits and hospital stays, thereby improving survival outcomes and alleviating healthcare costs.