An Ontological Framework for Lung Carcinoma Prognostication via Sophisticated Stacking and Synthetic Minority Oversampling Techniques

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, predominantly due to its late-stage diagnosis and rapid progression. This study proposes an advanced ensemble learning model for early detection of lung cancer by analyzing demographic, clinical, and lifestyle features from a comprehensive survey dataset. To mitigate the issue of class imbalance, the Adaptive Synthetic Sampling (ADASYN) technique is employed, generating synthetic data for the minority class and improving model fairness. The ensemble model integrates XGBoost, LightGBM, and CatBoost algorithms in a stacking framework, which leverages the strengths of each algorithm to enhance overall predictive accuracy. To further refine the model, feature selection techniques such as Recursive Feature Elimination with Cross-Validation (RFECV) and LASSO Regression are utilized, minimizing the risk of overfitting and ensuring the model focuses on the most important predictive features. Hyperparameter tuning is optimized using Optuna, which applies Bayesian optimization techniques to identify the best configuration for model performance. Additionally, Stratified K-Fold Cross-Validation is employed to validate the robustness of the model, ensuring balanced representation across folds and enhancing generalizability. This approach demonstrates significant improvements in prediction accuracy compared to traditional machine learning models, emphasizing the potential of combining ensemble learning with robust optimization techniques for early lung cancer detection, ultimately contributing to more effective clinical decision-making and intervention.