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• W3191925787 abstract "Malnutrition represents a major public health con-cern worldwide, and it particularly harms older adults since it is frequently associated with several chronic health disorders. It significantly increases in institutionalised subjects, especially in presence of cognitive impairments. For this reason, it is essential to early detect nutritional deficiencies and unhealthy dietary habits and to timely trigger proper feedback and interventions. Malnutrition assessment in clinical settings is generally based on standard screening tools including questionnaires, rating scales, and biometrics. Technological solutions based on IoT and mobile devices, along with AI techniques for data analysis, could provide important advantages in the risk assessment and prevention. In this paper we present a Decision Support System for the early detection of malnutrition risk based on data collected by a m-health application for nutritional and body composition monitoring. The application has been in use in a nursing home in Italy from March 2018 and, considering drop-outs and the impact of Covid-19 pandemic, we have been able to collect consistent data over three different trial periods. In collaboration with a medical specialist, we performed feature engineering to estimate daily intake for the major food components, meal completeness, variability, considering also physiological data. Then, we ran several Machine Learning models using the results of Mini Nutritional Assessment rating scale as ground truth, and applying SMOTE and cost-sensitive learning to deal with the dataset imbalance. Obtained results indicate that the best performing ML models for malnutrition risk prediction reach median accuracy and recall values of 94% and 92%, respectively." @default.
• W3191925787 created "2021-08-16" @default.
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• W3191925787 date "2021-06-01" @default.
• W3191925787 modified "2023-09-23" @default.
• W3191925787 title "Malnutrition Risk Assessment in Frail Older Adults using m-Health and Machine Learning" @default.
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• W3191925787 doi "https://doi.org/10.1109/icc42927.2021.9500471" @default.
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