Artificial Intelligence for Malnutrition Prediction: Integrating Clinical, Socioeconomic, and Environmental Determinants for Sustainable Development

A Conceptual Framework for AI-Integrated Metabolomics in Predictive Health Systems for Resource-Constrained Environments

The increasing prevalence of non-communicable diseases (NCDs) continues to place significant pressure on healthcare systems, particularly in low- and middle-income regions where access to early diagnostic infrastructure remains limited. Conventional healthcare approaches are often reactive, detecting diseases after substantial progression and reducing opportunities for timely intervention. This challenge highlights the need for predictive, affordable, and data-driven healthcare solutions that can support early diagnosis and prevention. This study proposes a conceptual framework that integrates metabolomics with artificial intelligence (AI) to support predictive health systems in resource-constrained environments. Metabolomics enables comprehensive characterization of small-molecule metabolites, providing valuable insights into physiological and pathological changes. When combined with machine learning approaches, metabolomic datasets can be analyzed to identify potential biomarkers, classify disease risks, and generate personalized healthcare insights. The proposed framework presents a multi-layered architecture consisting of metabolomic data acquisition, preprocessing, feature engineering, AI-based predictive modeling, and clinical decision-support outputs. The model emphasizes scalability through the integration of portable diagnostic technologies, cloud-based analytics, edge computing, and decentralized healthcare delivery approaches. It also considers critical implementation challenges, including data harmonization, infrastructure limitations, algorithmic bias, and ethical governance. Furthermore, the framework highlights the need for empirical validation through pilot studies, technology assessment, and multi-site evaluation to determine its feasibility, reliability, and applicability across diverse healthcare settings. By integrating biological data analysis, computational intelligence, and responsible innovation principles, this study provides a pathway toward accessible predictive and precision public health systems for underserved populations. Overall, this research contributes to the advancement of AI-enabled healthcare by proposing a scalable and context-sensitive model that bridges metabolomics, artificial intelligence, and healthcare delivery requirements in resource-constrained environments.

Artificial intelligence in maternal and child health: Current applications, translational gaps, and future research priorities

Artificial intelligence (AI) is rapidly transforming healthcare, with growing impact on maternal and child health (MCH) through advances in machine learning, deep learning, computer vision, generative models, and conversational systems. This article provides a comprehensive synthesis of current AI applications in MCH, structured across six key domains: predictive modeling, image analysis, deep learning and interpretability, generative and multi-omics approaches, conversational AI, and environmental and lifestyle analytics. Drawing on recent literature and the translational experience of the Spanish RICORS-SAMID network, we analyze how these technologies are being integrated into clinical, preventive, and assistive workflows. Across domains, AI demonstrates strong potential for early risk prediction (e.g., preeclampsia, fetal growth restriction, neonatal outcomes), automated image interpretation, biomarker discovery, and personalized decision support. However, despite promising performance metrics, most systems remain at the proof-of-concept stage, with limited external validation, scarce prospective evaluation, and incomplete integration into real-world clinical pathways. Key translational gaps include data heterogeneity, lack of interoperability, insufficient explainability, and challenges related to bias, fairness, and regulatory compliance. We argue that the next phase of AI in MCH must shift from static prediction toward longitudinal, mechanism-aware, and clinically actionable systems, supported by robust validation and multidisciplinary collaboration. Particular emphasis is placed on equity, as the benefits of AI must extend to low-resource settings where maternal and neonatal morbidity remains highest. By bridging technical innovation with clinical implementation, coordinated research networks such as RICORS can play a critical role in accelerating the safe, effective, and equitable deployment of AI in maternal and child healthcare.

Applying Artificial Intelligence and machine learning in precision nutrition

A key feature of the Precision Nutrition and Health approach is the ability to tailor interventions to individual variability using multimodal data from large-scale biobanks and cohorts. Artificial intelligence (AI) and machine learning (ML) models offer new potential to model complex data but remain constrained by challenges related to data quality, interpretability, validation, and causal inference. This Perspective synthesizes current AI/ML methodologies in PN, elucidates their interplay with the distinctive features of multi-omic and nutritional data, such as being compositional, episodic, context-dependent, and error-prone, and delineates nutrition-specific best practices for achieving robust, interpretable, and clinically actionable AI integration in research and practice. In this Perspective, the authors highlight critical challenges, knowledge gaps, and opportunities for robust, equitable, rigorous, reproducible, and actionable artificial intelligence integration in research and practice, and provide a roadmap and checklist for enabling the use of artificial intelligence in precision nutrition.

An Explainable Stacking Ensemble Model for Predicting Childhood Malnutrition among Under-Five Children in Nigeria

Childhood malnutrition remains a major public health challenge in Nigeria, contributing to child morbidity, mortality, impaired cognitive development, and poor long-term socioeconomic outcomes. Although machine learning has shown promise for malnutrition prediction, many models operate as black boxes, limiting their interpretability and adoption in clinical and public health decision-making. This study developed an explainable stacking ensemble model for classifying and predicting childhood malnutrition among children Under-five in Nigeria, using the 2023–24 Nigeria Demographic and Health Survey (NDHS). Data from 9,374 children with valid anthropometric measurements were analysed, with malnutrition status determined using WHO Child Growth Standards based on height-for-age, weight-for-age, and weight-for-height z-scores. Following preprocessing and a multi-method, multi-phenotype feature selection process combining Recursive Feature Elimination, Mutual Information, and Random Forest Feature Importance, twenty-two socioeconomic, demographic, geographic, maternal, and child-health variables were retained for modelling. A stacking ensemble comprising Random Forest, XGBoost, LightGBM, CatBoost, and Support Vector Machine as base learners, with Logistic Regression as meta-learner, was developed using mother-level stratified group cross-validation to prevent clustering-related data leakage. Within-fold SMOTE addressed class imbalance, and Youden's J statistic guided threshold optimisation. Model interpretability was achieved using SHAP, providing both global and individual-level explanations. The stacking ensemble outperformed all individual base learners, achieving a ROC-AUC of 0.750, F1-score of 0.662, recall of 72.6%, precision of 61.0%, and accuracy of 69.9% at the optimal threshold of 0.40. SHAP analysis identified household wealth index, maternal education, child age, and geographic location as the most influential predictors of malnutrition risk. These findings demonstrate that integrating ensemble learning with explainable artificial intelligence provides an accurate, transparent, and practical decision-support framework for early identification of at-risk children, supporting evidence-based nutrition interventions and public health policy in Nigeria.

MACHINE LEARNING FOR EARLY MATERNAL RISK STRATIFICATION IN UZBEKISTAN: DEVELOPMENT OF A GRADIENT BOOSTING MODEL BASED ON DHS DATA

Deep learning approaches show promise for predicting childhood malnutrition: A comparative study with traditional machine learning methods using survey data

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.