Enhanced Lung Cancer Detection using Ensemble Learning Algorithms: A Comparative Study of LightGBM & CatBoost

Lung cancer is a significant global health issue, and early detection of lung nodules is vital for timely diagnosis and treatment. Computed Tomography (CT) scans are instrumental in classifying malignant and benign nodules. This study explores the application of ensemble learning algorithms, specifically AdaBoost, XGBoost, CatBoost, and LightGBM, to increase the accuracy of lung malignancy identification. By assessing these models using criteria such as accuracy, sensitivity, and specificity, and specificity, the analysis demonstrates that both LightGBM and CatBoost individually achieve high performance, with accuracies of 97.11% and 97.88%, respectively. LightGBM shows a sensitivity of 96.3% and specificity of 97.64%, while CatBoost slightly outperforms it with a sensitivity of 97.28% and specificity of 98.28%. The combined use of LightGBM and CatBoost yields the highest efficacy of 99.34% accuracy, 99.52% sensitivity and 99.13% specificity. This ensemble approach leverages the strengths of both models, significantly enhancing detection accuracy and reliability, thereby presenting a highly effective solution for lung cancer detection.