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

Identificador:  imarina:9380776

Handle: https://hdl.handle.net/20.500.11797/imarina9380776

Autores:  Kumar Yadav, Gaurav; Moreno Vidales, Benigno; Duenas, Sara; Abdel-Nasser, Mohamed; Rashwan, Hatem A; Puig, Domenec; Nandi, G C

Resumen:
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).