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Using classification techniques for statistical analysis of Anemia

Kanak Meena1, Devendra K Tayal1, Vaidehi Gupta1

  • 1Computer Science and Engineering, India; Indira Gandhi Delhi Technical University for Women, India.

Artificial Intelligence in Medicine
|March 16, 2019
PubMed

Insights

This study uses data mining to predict childhood anemia, linking maternal health and diet during pregnancy to infant anemia. The findings aim to guide parents and clinicians in preventing this widespread nutritional deficiency.

Area of Science:

  • Public Health
  • Data Science
  • Pediatrics

Background:

  • Childhood anemia is a growing global health concern, often linked to a lack of awareness about its causes and prevention.
  • Existing methods for anemia prediction were time-consuming, relying on expert advice translated into algorithms.

Purpose of the Study:

  • To develop a decision support system for predicting childhood anemia using data mining techniques.
  • To investigate the relationship between maternal health and diet during pregnancy and the child's anemic status.
  • To provide dietary guidelines for anemia prevention in infants.

Main Methods:

  • Utilized data mining techniques, specifically decision tree and association rule mining.
  • Applied these techniques to a dataset from India's National Family Health Survey-4 (NFHS-4) (2015-16).
  • Compared the effectiveness of decision tree and association rule mining for anemia prediction.

Main Results:

  • Developed a predictive model for childhood anemia.
  • Established correlations between maternal nutritional factors and infant anemia.
  • Identified key feeding practices and dietary influences on infant health related to anemia.

Conclusions:

  • Data mining offers an efficient approach for anemia prediction and understanding risk factors.
  • Maternal health and diet during pregnancy significantly impact a child's risk of anemia.
  • The developed system can aid in preventing anemia by informing parents and clinicians about crucial dietary and feeding practices.

Keywords:
AnemiaAssociative classificationData miningDecision treeHealthcare

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