Automated Framework for Exploring Potential Biomarkers in Lung Cancer

Lung cancer has experienced a concerning increase in incidence with smoking, genetics, and air pollution identified as major contributing factors. Early detection is crucial for improving patient outcomes, as the disease can be difficult to diagnose in its early stages. This research presents a machine learning-based approach to predict lung cancer risk by identifying potential biomarkers. The proposed framework utilizes a streamlined pipeline of validated tools, ensuring a user-friendly and efficient process. By analyzing genetic data and identifying predictive biomarkers, this research aims to empower individuals with a family history of cancer or early symptoms to take proactive steps towards prevention and early detection. This approach contributes to the fight against lung cancer by providing valuable insights into disease risk and facilitating timely intervention.