Robust Stacked Ensemble Model for Lung Cancer Diagnosis

In this paper, we introduce a stacked ensemble model for lung cancer diagnosis, combining Random Forest, Support Vector Machine, and Artificial Neural Networks. Our model, validated on two datasets and achieving diagnostic accuracies of 92.6% and 88.9%, outperforms traditional algorithms. We also highlight the model's robustness through advanced feature selection methods like ReliefF and Chi-Square. The results we achieved showcase the potential of machine learning techniques in medical diagnostics and contribute significantly to the field of computational oncology, offering a promising approach for early lung cancer detection and prognosis.