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Effective ML-based quality of life prediction approach for dependent people in guardianship entities

  • Datos identificativos

    Identificador: imarina:9285923
    Autores:
    Yadav, Gaurav KumarVidales, Benigno MorenoRashwan, Hatem AOliver, JoanPuig, DomenecNandi, G CAbdel-Nasser, Mohamed
    Resumen:
    This paper proposes an effective approach for predicting quality of life (QoL) for dependent individuals in guardianship entities. In addition, it aims to improve the QoL of people with intellectual disabilities. The proposed QoL prediction approach employs machine learning (ML) techniques to model the relationship between eight aspects of QoL and the corresponding QoL index. It determines whether or not a person needs assistance based on the index value. The proposed approach determines the priority of care (PoC) value for each aspect of a person. Based on PoC, the deficit aspect is determined, followed by the type of assistance a person requires, based on the decision priorities. It also generates a support report with suggested actions to highlight the level in that aspect. In addition, we train multiple ML models to predict the standard score (SS), which represents the support value related to the eight aspects of QoL. Finally, we use SS values to train an ML model to predict the support intensity scale (SIS). On a dataset compiled from guardianship entities, the proposed approach is validated. The QoL index, SS, and SIS prediction models achieve average R2 values of 0.9897, 0.9998, and 0.9977 with a standard deviation of 0.0051, 0.0002, and 0.0007, respectively.
  • Otros:

    Autor según el artículo: Yadav, Gaurav Kumar; Vidales, Benigno Moreno; Rashwan, Hatem A; Oliver, Joan; Puig, Domenec; Nandi, G C; Abdel-Nasser, Mohamed
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: Abdellatif Fatahallah Ibrahim Mahmoud, Hatem / Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi / Yadav, Gaurav Kumar
    Palabras clave: Support intensity scale Quality of life Priority of care Machine learning Intellectual-disability Intellectual disability supports support intensity scale priority of care machine learning intellectual disability field adults
    Resumen: This paper proposes an effective approach for predicting quality of life (QoL) for dependent individuals in guardianship entities. In addition, it aims to improve the QoL of people with intellectual disabilities. The proposed QoL prediction approach employs machine learning (ML) techniques to model the relationship between eight aspects of QoL and the corresponding QoL index. It determines whether or not a person needs assistance based on the index value. The proposed approach determines the priority of care (PoC) value for each aspect of a person. Based on PoC, the deficit aspect is determined, followed by the type of assistance a person requires, based on the decision priorities. It also generates a support report with suggested actions to highlight the level in that aspect. In addition, we train multiple ML models to predict the standard score (SS), which represents the support value related to the eight aspects of QoL. Finally, we use SS values to train an ML model to predict the support intensity scale (SIS). On a dataset compiled from guardianship entities, the proposed approach is validated. The QoL index, SS, and SIS prediction models achieve average R2 values of 0.9897, 0.9998, and 0.9977 with a standard deviation of 0.0051, 0.0002, and 0.0007, respectively.
    Áreas temáticas: General engineering Farmacia Engineering, multidisciplinary Engineering (miscellaneous) Engineering (all)
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: gauravkumar.yadav@urv.cat mohamed.abdelnasser@urv.cat hatem.abdellatif@urv.cat gauravkumar.yadav@urv.cat domenec.puig@urv.cat
    Identificador del autor: 0000-0001-7022-290X 0000-0002-1074-2441 0000-0001-5421-1637 0000-0001-7022-290X 0000-0002-0562-4205
    Fecha de alta del registro: 2024-09-21
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://www.sciencedirect.com/science/article/pii/S1110016822006846
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Alexandria Engineering Journal. 65 909-919
    Referencia de l'ítem segons les normes APA: Yadav, Gaurav Kumar; Vidales, Benigno Moreno; Rashwan, Hatem A; Oliver, Joan; Puig, Domenec; Nandi, G C; Abdel-Nasser, Mohamed (2023). Effective ML-based quality of life prediction approach for dependent people in guardianship entities. Alexandria Engineering Journal, 65(), 909-919. DOI: 10.1016/j.aej.2022.10.028
    DOI del artículo: 10.1016/j.aej.2022.10.028
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2023
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Engineering (Miscellaneous),Engineering, Multidisciplinary
    Support intensity scale
    Quality of life
    Priority of care
    Machine learning
    Intellectual-disability
    Intellectual disability
    supports
    support intensity scale
    priority of care
    machine learning
    intellectual disability
    field
    adults
    General engineering
    Farmacia
    Engineering, multidisciplinary
    Engineering (miscellaneous)
    Engineering (all)
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