Unveiling predictive factors for household-level stunting in India: A machine learning approach using NFHS-5 and satellite-driven data.

Journal: Nutrition (Burbank, Los Angeles County, Calif.)
PMID: 39848008

Abstract

OBJECTIVES: Childhood stunting remains a significant public health issue in India, affecting approximately 35% of children under 5. Despite extensive research, existing prediction models often fail to incorporate diverse data sources and address the complex interplay of socioeconomic, demographic, and environmental factors. This study bridges this gap by employing machine learning methods to predict stunting at the household level, using data from the National Family Health Survey combined with satellite-driven datasets.

Authors

  • Prashant Kumar Arya
    Institute for Human Development, Delhi, India; ICSSR Post-Doctoral Fellow, Central University of Jharkhand, Ranchi, India. Electronic address: prashantarya5@gmail.com.
  • Koyel Sur
    Geospatial Resource Mapping and Application Group, Punjab Remote Sensing Centre, Punjab, India. Electronic address: Koyelsur3@gmail.com.
  • Tanushree Kundu
    Central University of Jharkhand, Ranchi, Jharkland, India. Electronic address: tanushree.kundu@cuj.ac.in.
  • Siddharth Dhote
    Institute for Human Development, Delhi, India. Electronic address: siddharthdhote28@gmail.com.
  • Shailendra Kumar Singh
    Central University of Jharkhand, Ranchi, Jharkland, India. Electronic address: shailendra.singh@cuj.ac.in.

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