Utilizing machine learning to predict hospital admissions for pediatric COVID-19 patients (PrepCOVID-Machine).

ABSTRACT: The COVID-19 pandemic has burdened healthcare systems globally. To curb high hospital admission rates, only patients with genuine medical needs are admitted. However, machine learning (ML) models to predict COVID-19 hospitalization in Asian children are lacking. This study aimed to develop and validate ML models to predict pediatric COVID-19 hospitalization. We collected secondary data with 2200 patients and 65 variables from Malaysian aged 0 to 12 with COVID-19 between 1st February 2020 and 31st March 2022. The sample was partitioned into training, internal, and external validation groups. Recursive Feature Elimination (RFE) was employed for feature selection, and we trained seven supervised classifiers. Grid Search was used to optimize the hyperparameters of each algorithm. The study ana

SUBMITTER: Liew CH 

PROVIDER: S-EPMC11760342 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature