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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.

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 R 2 values of 0.9897, 0.9998, and 0.9977 with a standard deviation of 0.0051, 0.0002, and 0.0007, respectively. [ABSTRACT FROM AUTHOR]

Highlights: [•] Three assessment tools are currently used in Taiwan: the medical diagnosis, the scales of ADL and IADL. [•] We evaluated the SIS as an assessment tool for funding patterns for people with ID. [•] SIS had higher correlations with the IADL than the ADL and medical diagnosis. [•] SIS can be a useful tool for resource allocation in terms of the support needs of users and saving funds. [ABSTRACT FROM AUTHOR]