An Efficient and Explainable Ensemble-learning Framework for Early Lung Cancer Biomarkers Detection

This study proposes an Efficient and Explainable Ensemble-learning framework (EEE-framework) designed for early detection of non-small cell lung cancer (NSCLC) biomarkers using gene expression data, specifically addressing challenges of interpretability in detection outcomes. The EEE-framework comprises two core modules. The first module constructs an ensemble-learning model balancing detection accuracy and interpretability. Considering the typical trade-off between accuracy and interpretability, we explore various combinations of 10 individual learners, ultimately selecting five (Decision Tree, Random Forest, XGBoost, AdaBoost, and Gaussian Naive Bayes) with high interpretability as base learners. Subsequently, we classify NSCLC by integrating voting-ensemble with high interpretability. The second module develops a multi-perspective approach to identify important NSCLC biomarkers using six explainable artificial intelligence (XAI) methods, ranging from Local to Global Interpretability and from Intrinsic to Post-hoc Interpretability. The EEE-framework's generalization ability and interpretability are validated using the TCGA RNA_seq public dataset and a self-constructed methylation dataset. Validation results demonstrate the exceptional classification performance of the ensemble-learning model integrated in the framework, achieving F1 values of 0.9983(AUC:0.9993) and 0.8462(AUC:0.9249) on the two datasets, respectively. Global and Local Interpretable Visualization results significantly enhance the diagnosis and understanding of NSCLC biomarkers, providing insights to their importance rankings, interrelationships, and causality with predicted outcomes.