Risk Identification and Relapse Prediction in Lung Adenocarcinoma (LUAD)

Lung adenocarcinoma (LUAD) is the most common type of non-small cell lung cancer (NSCLC) and poses significant challenges due to its high recurrence rate after treatment. This study explores risk identification and relapse prediction in LUAD using machine learning techniques, focusing on Support Vector Machines (SVM). Utilizing the LUAD MSKCC 2020 Clinical Data, we analyzed clinical, demographic, and genomic features to build a predictive model for cancer relapse. Our findings indicate that certain clinical parameters, such as tumor size and nodal involvement, are strongly associated with relapse risk. We identified a set of significant genetic markers and tumor mutation burdens that contribute to relapse prediction. The SVM model demonstrated promising accuracy in predicting relapse, highlighting its potential in improving patient stratification and personalized treatment approaches. The study emphasizes the importance of early detection and intervention strategies in enhancing patient outcomes and reducing recurrence rates. Future work will focus on refining the model and integrating additional data sources to further enhance its predictive capabilities.