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Updated: Aug 27, 2025

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Machine Learning Algorithms for understanding the determinants of under-five Mortality.

Rakesh Kumar Saroj1, Pawan Kumar Yadav2, Rajneesh Singh3

  • 1Department of Community Medicine, Sikkim Manipal Institute of Medical Sciences-Sikkim Manipal University, Gangtok, Sikkim, 737102, India. rakesh.saroj@bhu.ac.in.

Biodata Mining
|September 24, 2022
PubMed
Summary

Machine learning models accurately predict under-five mortality. Neural networks showed the highest accuracy, identifying key factors like mother's education and child's birth size for targeted interventions.

Keywords:
AccuracyMachine learningNeural NetworkRandom ForestUnder-five mortality

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Area of Science:

  • Public Health
  • Data Science
  • Biostatistics

Background:

  • Under-five mortality remains a critical global health and social development issue.
  • Predictive modeling can aid in identifying at-risk populations and informing interventions.

Purpose of the Study:

  • To evaluate the accuracy of various machine learning models in predicting under-five mortality.
  • To identify significant factors associated with under-five mortality.

Main Methods:

  • Utilized data from the National Family Health Survey (NFHS-IV) for Uttar Pradesh.
  • Applied machine learning techniques including logistic regression, decision trees, random forest, Naïve Bayes, KNN, SVM, neural networks, and ridge classifier.
  • Assessed model performance using metrics like accuracy, precision, recall, F1 score, Cohen's Kappa, and AUROC.

Main Results:

  • The neural network model demonstrated superior predictive accuracy for under-five mortality (95.29%–95.96%).
  • Logistic regression also performed well, with accuracy ranging from 94% to 95%.
  • Key factors identified include number of living children, survival time, wealth index, child size at birth, mother's education, and birth order.

Conclusions:

  • Neural networks offer a highly accurate approach for predicting under-five mortality.
  • Machine learning models, including logistic regression, provide valuable tools for analyzing high-dimensional health data.
  • Findings can inform targeted public health strategies to reduce child mortality.