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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Utilization of tree-based machine learning models for predicting low birth weight cases.

Flavio Leandro de Morais¹, Elisson da Silva Rocha¹, Gabriel Masson¹

¹Programa de Pós-Graduação em Engenharia da Computação (PPGEC), Universidade de Pernambuco (UPE), Recife, Pernambuco, Brazil.

BMC Pregnancy and Childbirth

|February 26, 2025

Summary

This summary is machine-generated.

Machine learning models can predict low birth weight (LBW) in newborns. Removing duplicate data and selecting key attributes improved model accuracy, highlighting socio-demographic factors and gestational history as crucial predictors.

Keywords:

Low birth weight Machine learning Prediction Prenatal care

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Area of Science:

Computational medicine
Neonatal health research
Machine learning in healthcare

Background:

Low birth weight (LBW) affects over 20 million newborns globally.
Machine learning (ML) offers potential for early LBW prediction and intervention.
Predictive models can guide treatment adjustments and dietary recommendations during pregnancy.

Purpose of the Study:

To evaluate machine learning models for predicting LBW risk in pregnant women.
To identify pregnant individuals at high risk for adverse neonatal outcomes related to LBW.

Main Methods:

Data analysis and attribute selection across four distinct scenarios.
Validation of five machine learning models using cross-validation and hyper-parameter optimization.
Performance evaluation using seven metrics and statistical analysis for LBW prediction effectiveness.

Main Results:

Model performance varied across scenarios; duplicate data removal improved recall (0.83) and F1-score (0.64).
Statistical analysis indicated significant differences (p < 0.05) in model performance.
Attribute importance analysis identified key predictive factors.

Conclusions:

Removing duplicate data and careful attribute selection enhance ML model performance for LBW prediction.
Socio-demographic characteristics and gestational history are the most influential factors in model training.
Optimized ML models can aid in identifying at-risk pregnancies for timely intervention.