Using Multiple Machine Learning Algorithms for Cancer Prognosis in Lung Adenocarcinoma

Lung cancer is the most prevailing source of death due to cancer, accounting for over 25% of death in the United States. Being able to predict the survival time for patients will provide valuable information for the choice of their treatment plans and benefit patient management. With the advancement of next-generation sequencing, many high-throughput sequencing data for DNA and RNA becomes available for cancer patients. Here we present the results for using multiple machine learning algorithms in predicting the survivorship of patients with Lung cancer adenocarcinoma. Using the publicly available datasets in TCGA with the overall survival length, and transcriptomic information, we evaluated our ability to predict prognosis. We found that using the expression level of a few candidate genes alone generates significant statistical power from a very limited number of patients, suggesting more future studies to be conducted on collecting such data to facilitate personalized medicine.