Development of a machine learning model for prediction of intraventricular hemorrhage in premature neonates.

Purpose: Intraventricular hemorrhage (IVH) is a common and severe complication in premature neonates, leading to long-term neurological impairments. Early prediction and identification of risk factors for IVH in premature neonates are crucial for improving clinical outcomes. This study aimed to predict IVH in premature neonates and determine risk factors using machine learning (ML) algorithms.