In this study, we proposed a comprehensive, noninvasive machine learning approach utilizing the classifier algorithms and feature selection methods to robustly predict EGFR and KRAS mutations among patients with NSCLC based on medical radiomics features. The gene discussed is KRAS; the disease is non-small cell lung carcinoma.