Improving Interpretability in Lung Cancer Prediction through Explainable Ensemble Voting Approach

• Presented an Explainable Ensemble Voting Approach (EEVA) which combines Random Forest (RF), Decision Tree (DT) and Multi-Layer Perceptron (MLP) using soft voting, to forecast lung cancer.

• Utilized LIME and SHAP to generate local and global explanations, enhancing model transparency and interpretability.

• Achieved an accuracy of 92.86%, outperforming baseline methods and demonstrating strong potential for clinical application.