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Predictive Modelling of Low Birth Weight in Pregnancies: A Comparative Analysis of Logistic Regression and Decision

Ravi Kumar1, Abhinav Bahuguna2, Palak Goyal1

  • 1Department of Community Medicine, Shri Ram Murti Smarak Institute of Medical Sciences, Bareilly, India.

Indian Journal of Community Medicine : Official Publication of Indian Association of Preventive & Social Medicine
|September 15, 2025
PubMed

Insights

Predicting low birth weight (LBW) is crucial for infant health. Maternal age, comorbidities, and gestational age significantly predict LBW, with logistic regression showing higher accuracy than decision trees.

Area of Science:

  • Maternal and Child Health
  • Biostatistics
  • Predictive Modeling

Background:

  • Birth weight is critical for infant development.
  • Low birth weight (LBW) infants face significant early health challenges.
  • Identifying LBW predictors is essential for targeted interventions.

Purpose of the Study:

  • To identify significant predictors of LBW using a model-based approach.
  • To compare the predictive performance of logistic regression and decision tree models for LBW.

Main Methods:

  • Hospital-based cross-sectional study of 130 pregnant women (2022-2023).
  • Application of logistic regression and decision tree methods.
  • Model performance assessed using Receiver Operating Characteristic (ROC) curves.

Main Results:

  • Prevalence of LBW was 38.5%.
  • Significant predictors of LBW include maternal age, abortion history, comorbidities, pregnancy complications, and gestational age (P < 0.05).
  • Logistic regression (AUC=0.881) and decision tree (AUC=0.814) models demonstrated good discrimination ability.

Conclusions:

  • Logistic regression demonstrated higher accuracy in predicting LBW compared to the decision tree model.
  • Findings underscore the need for targeted maternal and child care policies to mitigate LBW risks.
  • Decision trees, while useful for pattern recognition, require cautious application due to potential overfitting.
Abstract

Keywords:
Low birth weightarea under the ROC curvedecision treelogistic regressionreceiving operating characteristic curve

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