Utilization of tree-based machine learning models for predicting low birth weight cases.

ABSTRACT:

Background

Low birth weight (LBW) is a health condition that affects over 20 million gestational outcomes worldwide. The current literature indicates that machine learning models have the potential to assist healthcare professionals in predicting LBW and giving them the opportunity to intervene earlier in the pregnancy, which might include adjusting medical treatments or suggesting changes in diet.

Purpose

This study proposes the evaluation of machine learning models to predict which pregnant women are at risk of neonatal outcomes with LBW.

Methods

The methodology involves six phases, including data analysis and attribute selection through different techniques, which generated four distinct scenarios. We used five machine learning models and validated them through cro

SUBMITTER: de Morais FL 

PROVIDER: S-EPMC11863662 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature