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Studying infant mortality: A demographic analysis based on data mining models.

Muhammad Islam Satti1, Mir Wajid Ali1, Azeem Irshad2

  • 1Department of Computer Science, Millennium Institute of Technology & Entrepreneurship (MiTE), Karachi, Pakistan.

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Summary
This summary is machine-generated.

Child mortality remains a global concern, especially in Pakistan and Ethiopia. This study uses data mining to identify key factors, achieving 97.8% accuracy in predicting child deaths.

Keywords:
data analyticsdemographic health surveyrule induction

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Area of Science:

  • Public Health
  • Data Science in Healthcare
  • Demographics

Background:

  • Child mortality under five years remains a significant global health challenge, particularly in developing nations like Pakistan and Ethiopia.
  • Despite global efforts, high mortality rates persist, necessitating advanced analytical approaches for effective intervention.
  • Predictive analytics offers a powerful tool for understanding and mitigating child mortality trends.

Purpose of the Study:

  • To identify and categorize the critical factors contributing to child mortality in Pakistan and Ethiopia using data mining techniques.
  • To develop a predictive model for child death rates based on demographic and health survey data.
  • To highlight the importance of data-driven insights for improving infant health outcomes.

Main Methods:

  • Utilized datasets from the Pakistan Demographic Health Survey and Ethiopian Demographic Health Survey.
  • Applied various data mining techniques including Bayesian network, J-48 (tree), PART (rule induction), random forest, and multi-level perceptron.
  • Evaluated the performance of multiple classifiers to determine the most accurate predictive model for child mortality.

Main Results:

  • Analysis of 12,654 (Pakistan) and 12,869 (Ethiopia) records identified key influencing factors on child mortality.
  • The best performing model achieved an average accuracy of 97.8% in forecasting child death frequency.
  • The developed model demonstrates the capability to estimate under-five mortality rates in the studied regions.

Conclusions:

  • Data mining techniques effectively identify critical factors driving child mortality in Pakistan and Ethiopia.
  • A highly accurate predictive model for child mortality has been developed, offering valuable insights for public health interventions.
  • An online forecasting tool based on this research is recommended to aid healthcare strategies and reduce child deaths.