ARTIFICIAL INTELLIGENCE-BASED MODEL FOR STUNTING RISK PREDICTION AND GROWTH MONITORING USING CHILD ANTHROPOMETRIC DATA
DOI:
https://doi.org/10.47701/2k8vdk50Keywords:
artificial intelligence, stunting, risk prediction, growth monitoring, anthropometric data.Abstract
Stunting remains a major public health problem that
requires early identification and continuous growth
monitoring. This study developed an artificial intelligence-
based model for stunting risk prediction and child growth
monitoring using anthropometric data. The proposed
framework integrates stunting detection, growth
monitoring, risk prediction, nutrition analysis, and health
education through an AI Engine consisting of data input,
data processing, feature extraction, AI modeling, and risk
output. A total of 60 child records were evaluated using four
anthropometric configurations: Weight-for-Age, Height-
for-Age, Head Circumference-for-Age, and a combination of
Weight, Height, and Head Circumference according to age.
The results showed accuracies of 85.1%, 86.0%, 90.6%, and
82.3%, respectively. Head Circumference-for-Age achieved
the highest accuracy of 90.6% in the evaluated dataset.
These findings demonstrate the potential of
anthropometric data and artificial intelligence for
supporting stunting risk prediction and child growth
monitoring. Further validation using larger and longitudinal
datasets is required to establish model robustness and
generalizability.
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