An ensemble learning with active sampling to predict the prognosis of postoperative non-small cell lung cancer patients.

ABSTRACT: Lung cancer is the leading cause of cancer death worldwide. Prognostic prediction plays a vital role in the decision-making process for postoperative non-small cell lung cancer (NSCLC) patients. However, the high imbalance ratio of prognostic data limits the development of effective prognostic prediction models. In this study, we present a novel approach, namely ensemble learning with active sampling (ELAS), to tackle the imbalanced data problem in NSCLC prognostic prediction. ELAS first applies an active sampling mechanism to query the most informative samples to update the base classifier to give it a new perspective. This training process is repeated until no enough samples are queried. Next, an internal validation set is employed to evaluate the base classifiers, and the ones with the

SUBMITTER: Hu D 

PROVIDER: S-EPMC9487160 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature