LUNG CANCER RELAPSE PREDICTION USING PARALLEL XGBOOST

Bioinformation

Abstract

Lung cancer has been the most popular form of cancer for decades. Surgery will offer the non-small cell lung cancer (NSCLC) patients the best hope of a cure if the cancer is diagnosed in the early stage. However, many patients eventually die of their disease due to relapse after surgery. Because of no symptoms of lung cancer in its early stage, many researchers try to improve methods to predict lung cancer relapse early. This study proposed a method to predict lung cancer relapse more accurately. This method has three stages; feature selection, parallel extreme gradient boost (XGBoost) classifications with different hyperparameters, and selection stage. It used two datasets of a gene expression microarray for different lung cancer types with its clinical information. The accuracy of the proposed model is excellent compared to other machine learning.