Author, as appears in the article.: Yadav, Gaurav Kumar; Vidales, Benigno Moreno; Rashwan, Hatem A; Oliver, Joan; Puig, Domenec; Nandi, G C; Abdel-Nasser, Mohamed
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: 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
Abstract: 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.
Thematic Areas: General engineering Farmacia Engineering, multidisciplinary Engineering (miscellaneous) Engineering (all)
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: gauravkumar.yadav@urv.cat mohamed.abdelnasser@urv.cat hatem.abdellatif@urv.cat gauravkumar.yadav@urv.cat domenec.puig@urv.cat
Author identifier: 0000-0001-7022-290X 0000-0002-1074-2441 0000-0001-5421-1637 0000-0001-7022-290X 0000-0002-0562-4205
Record's date: 2024-09-21
Papper version: info:eu-repo/semantics/publishedVersion
Link to the original source: https://www.sciencedirect.com/science/article/pii/S1110016822006846
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Alexandria Engineering Journal. 65 909-919
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
Article's DOI: 10.1016/j.aej.2022.10.028
Entity: Universitat Rovira i Virgili
Journal publication year: 2023
Publication Type: Journal Publications
Repositori URV