aiqbench analysis
This comparative study evaluates deep learning and traditional machine learning models for predicting childhood malnutrition in Nepal, identifying TabNet as the top performer due to its attention mechanisms on tabular survey data. By analyzing 16 algorithms against metrics like F1-score and recall, the research highlights key predictors such as maternal education and household wealth while offering a scalable framework for low-resource settings. The work advances practical applications of ML in public health by demonstrating how complex models can optimize case-finding where conventional methods are unavailable.
Deep learning approaches show promise for predicting childhood malnutrition: A comparative study with traditional machine learning methods using survey data
arXiv:2602.10381v2 Announce Type: replace Abstract: Childhood malnutrition remains a major public health concern in Nepal and other low-resource settings, while conventional case-finding approaches are labor-intensive and frequently unavailable in remote areas. This study provides one of the first applications of machine learning and deep learning to identify child malnutrition in Nepal. We systematically compared 16 algorithms spanning deep learning, gradient boosting, and traditional machine learning families, using data from the Nepal Multiple Indicator Cluster Survey (MICS) 2019. A composite malnutrition indicator was constructed by integrating stunting, wasting, and underweight status, and model performance was evaluated using ten metrics, with emphasis on F1-score and recall to account for substantial class imbalance and the high cost of failing to detect malnourished children. Among all models, TabNet achieved the highest scores among evaluated models, likely attributable to its attention-based architecture. A consensus feature importance analysis identified maternal education, household wealth index, and child age as the primary predictors of malnutrition, followed by geographic characteristics, vaccination status, and meal frequency. Collectively, these results demonstrate a scalable, survey-based screening framework for identifying children at elevated risk of malnutrition and for guiding targeted nutritional interventions. The proposed approach supports Nepal's progress toward the Sustainable Development Goals and offers a transferable methodological template for similar low-resource settings globally.
Abstract:Childhood malnutrition remains a major public health concern in Nepal and other low-resource settings, while conventional case-finding approaches are labor-intensive and frequently unavailable in remote areas. This study provides one of the first applications of machine learning and deep learning to identify child malnutrition in Nepal. We systematically compared 16 algorithms spanning deep learning, gradient boosting, and traditional machine learning families, using data from the Nepal Multiple Indicator Cluster Survey (MICS) 2019. A composite malnutrition indicator was constructed by integrating stunting, wasting, and underweight status, and model performance was evaluated using ten metrics, with emphasis on F1-score and recall to account for substantial class imbalance and the high cost of failing to detect malnourished children. Among all models, TabNet achieved the highest scores among evaluated models, likely attributable to its attention-based architecture. A consensus feature importance analysis identified maternal education, household wealth index, and child age as the primary predictors of malnutrition, followed by geographic characteristics, vaccination status, and meal frequency. Collectively, these results demonstrate a scalable, survey-based screening framework for identifying children at elevated risk of malnutrition and for guiding targeted nutritional interventions. The proposed approach supports Nepal's progress toward the Sustainable Development Goals and offers a transferable methodological template for similar low-resource settings globally.
Machine Learning (cs.LG)
arXiv:2602.10381 [cs.LG]
(or arXiv:2602.10381v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2602.10381 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1371/journal.pone.0353969 Focus to learn more DOI(s) linking to related resources
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