|Tipo di tesi||Tesi di laurea magistrale|
|Titolo||Interpretable Machine Learning Approach for Intrinsic Capacity prediction in Healthy Aging|
|Titolo in inglese|
|Struttura||Dipartimento di Scienze Fisiche, Informatiche e Matematiche|
|Corso di studi||INFORMATICA (D.M. 270/04)|
|Data inizio appello||2019-10-24|
|Disponibilità||Accessibile via web (tutti i file della tesi sono accessibili)|
World Health Organization (WHO) is searching for healthy aging tools to support healthcare professionals and self-management in routine care of older people. Healthy aging construct is based on Intrinsic Capacity (IC), defined as the composite of all the physical and mental capacities of an individual divided in 5 domains: locomotion, cognition, psychological, vitality and sensory. My thesis is art of a joint project which final aim is the definition of an IC by means of a data driven approach. This project used data generated by the MySAwH study. Data collected from this study was processed using big data analytics to generate a machine learning prediction model of helthy aging, so called Intrinsic Capacity (IC).