Evaluation of stacked ensemble model performance to predict clinical outcomes: A COVID-19 study.

ABSTRACT:

Background

The application of machine learning (ML) to analyze clinical data with the goal to predict patient outcomes has garnered increasing attention. Ensemble learning has been used in conjunction with ML to improve predictive performance. Although stacked generalization (stacking), a type of heterogeneous ensemble of ML models, has emerged in clinical data analysis, it remains unclear how to define the best model combinations for strong predictive performance. This study develops a methodology to evaluate the performance of "base" learner models and their optimized combination using "meta" learner models in stacked ensembles to accurately assess performance in the context of clinical outcomes.

Methods

De-identified COVID-19 data was obtained from the University of Louis

SUBMITTER: Kablan R 

PROVIDER: S-EPMC10165871 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature