  Malnutrition is a serious and prevalent health problem in the older
population, and especially in hospitalised or institutionalised subjects.
Accurate and early risk detection is essential for malnutrition management and
prevention. M-health services empowered with Artificial Intelligence (AI) may
lead to important improvements in terms of a more automatic, objective, and
continuous monitoring and assessment. Moreover, the latest Explainable AI (XAI)
methodologies may make AI decisions interpretable and trustworthy for end
users. This paper presents a novel AI framework for early and explainable
malnutrition risk detection based on heterogeneous m-health data. We performed
an extensive model evaluation including both subject-independent and
personalised predictions, and the obtained results indicate Random Forest (RF)
and Gradient Boosting as the best performing classifiers, especially when
incorporating body composition assessment data. We also investigated several
benchmark XAI methods to extract global model explanations. Model-specific
explanation consistency assessment indicates that each selected model
privileges similar subsets of the most relevant predictors, with the highest
agreement shown between SHapley Additive ExPlanations (SHAP) and feature
permutation method. Furthermore, we performed a preliminary clinical validation
to verify that the learned feature-output trends are compliant with the current
evidence-based assessment.
