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Predictive Modeling to Identify Stunting Risk in Children.

Siti Rahayu Nadhiroh1, Armedy Ronny Hasugian2, Allisa Nadhira Permata Arinda Putri1

  • 1Department of Nutrition, Faculty of Public Health, Universitas Airlangga, Surabaya, Indonesia.

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Summary

Indonesia faces a high childhood stunting rate. A predictive model identified key risk factors like age, birth weight, and breastfeeding, achieving 73.8% accuracy in identifying children at risk.

Keywords:
childrenprediction modelriskstunting

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Area of Science:

  • Public Health
  • Pediatrics
  • Data Science

Background:

  • Indonesia continues to face a significant burden of childhood stunting, with profound short- and long-term health and economic consequences.
  • Stunting impacts include increased morbidity, mortality, impaired growth, higher chronic disease risk, and reduced future productivity.

Purpose of the Study:

  • To identify the primary risk factors associated with stunting in Indonesian children.
  • To develop and evaluate a predictive model for identifying children at risk of stunting.

Main Methods:

  • Analysis of the 2018 Indonesian Basic Health Research database, including 13,106 children under 5 and their mothers.
  • Bivariate analysis to identify significant risk factors, followed by a decision tree model for prediction.
  • Receiver Operating Characteristic (ROC) curve analysis to evaluate model performance.

Main Results:

  • The national stunting rate was 25.8%.
  • Key predictors identified by the decision tree model include age, sex, birth weight, birth length, maternal education, handwashing habits, and exclusive breastfeeding.
  • The predictive model achieved 73.8% accuracy, with an Area Under the Curve (AUC) of 63.7% on the ROC curve.

Conclusions:

  • The developed prediction model demonstrates acceptable accuracy in assessing stunting risk.
  • The decision tree model effectively differentiates between stunted and non-stunted children across various age groups, as supported by ROC curve analysis.