Articles producció científica> Enginyeria Informàtica i Matemàtiques

Predicting Personalized Quality of Life of an Intellectually Disabled Person Utilizing Machine Learning

  • Identification data

    Identifier: imarina:9380776
    Authors:
    Kumar Yadav, GauravMoreno Vidales, BenignoDuenas, SaraAbdel-Nasser, MohamedRashwan, Hatem APuig, DomenecNandi, G C
    Abstract:
    This work aims to enhance dependent persons' quality of life (QOL) by examining various aspects of their lives and providing the required assistance to enhance each aspect of their QOL. We employ machine learning methods to evaluate the eight aspects of QOL and forecast the corresponding index value. Machine learning algorithms input eight aspects of QOL and predict the QOL index value. The QOL Index value says the requirement of the support to a person, and it depends on eight aspects of the QOL. We use our dataset to train the machine learning model. Dataset is collected using the GENCAT scale tool, which takes 69 items and provides the score value for each aspect of the QOL. We apply many linear and nonlinear machine learning regression algorithms. The multiple linear regression algorithm results show better performance for root mean squared error (1.4729) and R-2 score (0.9918).
  • Others:

    Author, as appears in the article.: Kumar Yadav, Gaurav; Moreno Vidales, Benigno; Duenas, Sara; Abdel-Nasser, Mohamed; Rashwan, Hatem A; Puig, Domenec; Nandi, G C
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: Abdellatif Fatahallah Ibrahim Mahmoud, Hatem / Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi / Yadav, Gaurav Kumar
    Keywords: And machine learnin Intellectual and developmental disability Machine learning Priority of care Quality of life Support paradigm
    Abstract: This work aims to enhance dependent persons' quality of life (QOL) by examining various aspects of their lives and providing the required assistance to enhance each aspect of their QOL. We employ machine learning methods to evaluate the eight aspects of QOL and forecast the corresponding index value. Machine learning algorithms input eight aspects of QOL and predict the QOL index value. The QOL Index value says the requirement of the support to a person, and it depends on eight aspects of the QOL. We use our dataset to train the machine learning model. Dataset is collected using the GENCAT scale tool, which takes 69 items and provides the score value for each aspect of the QOL. We apply many linear and nonlinear machine learning regression algorithms. The multiple linear regression algorithm results show better performance for root mean squared error (1.4729) and R-2 score (0.9918).
    Thematic Areas: Artificial intelligence Ciências agrárias i Comunicació i informació Engenharias iii Engenharias iv General o multidisciplinar Información y documentación Interdisciplinar Medicina ii
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: domenec.puig@urv.cat gauravkumar.yadav@urv.cat hatem.abdellatif@urv.cat mohamed.abdelnasser@urv.cat gauravkumar.yadav@urv.cat
    Author identifier: 0000-0002-0562-4205 0000-0001-7022-290X 0000-0001-5421-1637 0000-0002-1074-2441 0000-0001-7022-290X
    Record's date: 2024-09-21
    Papper version: info:eu-repo/semantics/publishedVersion
    Papper original source: Frontiers In Artificial Intelligence And Applications. 356 139-142
    APA: Kumar Yadav, Gaurav; Moreno Vidales, Benigno; Duenas, Sara; Abdel-Nasser, Mohamed; Rashwan, Hatem A; Puig, Domenec; Nandi, G C (2022). Predicting Personalized Quality of Life of an Intellectually Disabled Person Utilizing Machine Learning. Amsterdam: IOS Press
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2022
    Publication Type: Proceedings Paper
  • Keywords:

    Artificial Intelligence
    And machine learnin
    Intellectual and developmental disability
    Machine learning
    Priority of care
    Quality of life
    Support paradigm
    Artificial intelligence
    Ciências agrárias i
    Comunicació i informació
    Engenharias iii
    Engenharias iv
    General o multidisciplinar
    Información y documentación
    Interdisciplinar
    Medicina ii
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