The impact of feature combinations on machine learning models for in-hospital mortality prediction.

ABSTRACT: The growing volume of healthcare data presents opportunities for machine learning to improve treatment, uncover new patterns in data and predict patient outcomes. Selecting appropriate features for a machine learning model is an important step in the process as the choice of relevant variables directly influences the model's performance and interpretability. Effective feature selection can enhance both the accuracy and generalisability of the model, especially given the complexity and heterogeneity of healthcare data. The XGBoost algorithm is trained on the eICU Collaborative Research Database to predict in-hospital mortality, with focus on investigating the impact of different feature sets. The analysis cohort comprised 73 210 patients. Different models are trained and tested using 20 000

SUBMITTER: Stenwig E 

PROVIDER: S-EPMC12594765 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature