Explainable AI for Malnutrition Risk Prediction from m-Health and Clinical Data

TL;DR

This paper introduces an explainable AI framework using heterogeneous m-health data for early malnutrition risk detection in older adults, emphasizing interpretability and clinical validation.

Contribution

It presents a novel AI approach combining multiple XAI methods for transparent malnutrition risk prediction from diverse health data.

Findings

Random Forest and Gradient Boosting classifiers perform best.

Incorporating body composition improves prediction accuracy.

SHAP and permutation methods show high explanation consistency.

Abstract

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.