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

• URL: http://arxiv.org/abs/2305.19636v1
• Date: Wed, 31 May 2023 08:07:35 GMT
• Title: Explainable AI for Malnutrition Risk Prediction from m-Health and Clinical Data
• Authors: Flavio Di Martino, Franca Delmastro, Cristina Dolciotti
• Abstract summary: 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. We also investigated several benchmark XAI methods to extract global model explanations.
• Score: 3.093890460224435
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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