Uncovering early predictors of cerebral palsy through the application of machine learning: a case-control study.

ABSTRACT:

Objective

Cerebral palsy (CP) is a group of neurological disorders with profound implications for children's development. The identification of perinatal risk factors for CP may lead to improved preventive and therapeutic strategies. This study aimed to identify the early predictors of CP using machine learning (ML).

Design

This is a retrospective case-control study, using data from the two population-based databases, the Slovenian National Perinatal Information System and the Slovenian Registry of Cerebral Palsy. Multiple ML algorithms were evaluated to identify the best model for predicting CP.

Setting

This is a population-based study of CP and control subjects born into one of Slovenia's 14 maternity wards.

Participants

A total of 382 CP cases, born between

SUBMITTER: Rapuc S 

PROVIDER: S-EPMC11367350 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature