A Machine Learning Framework for Early Detection of Lung Cancer using Gene Expression Data

Abstract—Early detection of lung cancer significantly improves treatment outcomes. This research presents a machine learning approach utilizing RNA sequencing and gene expression profiles to predict lung cancer presence with high accuracy. We preprocess high-dimensional biological data, apply feature selection techniques to identify relevant genetic markers, and develop classification models using ensemble learning methods. Experiments on publicly available datasets demonstrate that the proposed system achieves robust predictive performance, offering a promising tool for precision oncology.