Evaluating LIME and SHAP in Explaining Malnutrition Classification in Children Under Five

Nuru Nabuuso

2025

Abstract

Malnutrition in children under five is a significant public health issue in Uganda, with severe impacts on development and mortality. This paper explores machine learning (ML) models—Support Vector Machines (SVM), eXtreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANNs) — to predict malnutrition, and reports that XGBoost shows highest predictive accuracy. While the findings on XGBoost employed global model interpretation through feature importance based on permutations, we also introduce SHapley Additive exPlanations (SHAP) for both local and global interpretations. We follow with a focus on SHAP summary plots and bar charts to evaluate feature importance globally. In addition, we report on the comparison between SHAP and Local Interpretable Model-agnostic Explanations (LIME) to analyze the consistency of local explanations provided by both techniques. By contrasting LIME and SHAP, we advance the alignment between local and global interpretations in the context of XGBoost predictions. This comparison highlights the strengths and limitations of each method. Our findings aim to enhance the transparency of ML models and improve decision-making in child health interventions, providing significant insights into public health and ML interpretability.

Paper Citation

in Harvard Style

Nabuuso N. (2025). Evaluating LIME and SHAP in Explaining Malnutrition Classification in Children Under Five. In Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-730-6, SciTePress, pages 291-298. DOI: 10.5220/0013186500003905

in Bibtex Style

@conference{icpram25,
author={Nuru Nabuuso},
title={Evaluating LIME and SHAP in Explaining Malnutrition Classification in Children Under Five},
booktitle={Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2025},
pages={291-298},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013186500003905},
isbn={978-989-758-730-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Evaluating LIME and SHAP in Explaining Malnutrition Classification in Children Under Five
SN - 978-989-758-730-6
AU - Nabuuso N.
PY - 2025
SP - 291
EP - 298
DO - 10.5220/0013186500003905
PB - SciTePress