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

Abstract

Background: Birth weight plays a vital role in an infant's comprehensive development. Low birth weight (LBW) infants may go through several kinds of health complications in the early stages of their lives. This paper is an attempt to identify the predictors that significantly influence the likelihood of LBW through a model-based approach.

Methodology: Data for this hospital based cross sectional study includes 130 pregnant women during the years 2022-2023. We have applied logistic regression and the decision tree method for predicting LBW in pregnancies. The performance of these predictive models has been assessed through receiving operating characteristic curve (ROC).

Results: The findings revealed 38.5% prevalence of LBW in pregnancies. Factors such as age of mother, abortion, presence of co-morbidities, pregnancy complications, and gestational age have been identified as significant predictors (P < 0.05) of LBW through logistic regression. The area under the ROC curve (AUC=0.881) for logistic regression and decision tree (AUC=0.814) indicates that the fitted models have better discrimination ability.

Conclusions: Logistic have better accuracy than decision tree model. Decision tree excels at capturing patterns but may overfit and hence should be used with caution. This study highlighted the need of targeted policy implementation on maternal and childhood care to reduce the risk of LBW.