Lung cancer risk prediction using interpretable ensemble models on lifestyle and clinical data

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, where early detection and reliable risk assessment are critical for improving outcomes. This study develops and evaluates a range of ensemble learning models for lung cancer prediction using lifestyle and clinical indicators, with an emphasis on both predictive performance and interpretability. Five base learners—logistic regression, k-nearest neighbors, naïve Bayes, support vector machine, and linear discriminant analysis—were used to construct multiple boosting and bagging models. Building on these, voting and stacking ensembles were designed by selectively combining high-performing models. All approaches were evaluated on the original dataset as well as on balanced and upsampled variants derived through synthetic augmentation. Model performance was assessed using accuracy, precision, recall, F1-score, Matthews correlation coefficient (MCC), and AUC-ROC. The results show that ensemble approaches consistently outperform individual models, with voting and stacking demonstrating superior performance over boosting and bagging methods. The stacking model achieved the strongest overall performance across all evaluated models. On the original dataset, which provides a more realistic representation of practical deployment conditions, it attained an accuracy of 93.53%. Performance further improved on the balanced and upsampled datasets on the upsampled dataset. To enhance transparency, SHAP and LIME were employed to provide global and local interpretability, respectively, enabling identification of key clinical factors and patient-specific risk drivers. The analysis highlights both alignment with known clinical patterns and dataset-driven variations, supporting informed interpretation of model outputs. The results suggest that stacking-based ensembles can improve risk prediction from lifestyle and clinical indicators while maintaining model transparency through SHAP and LIME explanations. These findings highlight the potential of interpretable ensemble learning as a decision-support tool for lung cancer risk assessment.