Integrating Advanced Techniques: RFE-SVM Feature Engineering and Nelder-Mead Optimized XGBoost for Accurate Lung Cancer Prediction

Early detection of lung cancer is crucial for improving patient survival and reducing mortality. However, medical datasets often face challenges like irrelevant features and class imbalance, complicating accurate predictions. This study presents a comprehensive AI-powered lung cancer classification approach that enhances predictive accuracy and treatment planning. Our methodology combines Recursive Feature Elimination with Support Vector Machines (RFE-SVM) for effective feature selection and employs the XGBoost ensemble learning algorithm for classification, optimized using the Nelder-Mead algorithm. Evaluating the model’s generalizability on two distinct lung cancer datasets, results show that our approach outperforms traditional machine learning models, achieving 100% accuracy. This research highlights the importance of advanced computational techniques in healthcare, paving the way for more personalized and effective patient care.