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Predicting risks of low birth weight in Bangladesh with machine learning

S M Ashikul Islam Pollob1, Md Menhazul Abedin1, Md Touhidul Islam1

  • 1Statistics Discipline, Khulna University, Khulna, Bangladesh.

Plos One
|May 26, 2022
PubMed

Insights

Low birth weight (LBW) is a major concern in Bangladesh. Machine learning models, particularly logistic regression, accurately identified risk factors and predicted LBW, aiding in targeted interventions.

Area of Science:

  • Public Health
  • Biostatistics
  • Machine Learning in Healthcare

Background:

  • Low birth weight (LBW) is a significant contributor to child mortality and long-term health issues, particularly in developing nations like Bangladesh.
  • Identifying risk factors and improving prediction of LBW are crucial for public health initiatives.

Purpose of the Study:

  • To determine the key risk factors associated with low birth weight in Bangladesh.
  • To develop and evaluate machine learning algorithms for predicting low birth weight babies.

Main Methods:

  • Utilized data from the Bangladesh Demographic and Health Survey (2017-18) with 2351 respondents.
  • Employed binary logistic regression to identify risk factors and logistic regression and decision tree classifiers for prediction.
  • Model performance was assessed using accuracy, sensitivity, specificity, PPV, NPV, and AUC.

Main Results:

  • The prevalence of low birth weight in Bangladesh was found to be 16.2%.
  • Significant risk factors included respondent's region, education, wealth index, height, twin status, and number of alive children.
  • The logistic regression classifier achieved 87.6% accuracy and an AUC of 0.59, outperforming the decision tree classifier.

Conclusions:

  • The logistic regression classifier demonstrated superior accuracy in classifying low birth weight babies.
  • Findings underscore the need for integrated, cost-effective strategies to reduce and predict low birth weight in Bangladesh.
Abstract

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