Developing a model to predict the Social Activity Participation of the senior citizens living in South Korea by Combining Artificial Neural Network and QUEST Algorithm - Byeon, Haewon (International Journal of Engineering and Technology, 2019)
Social participation in old age is important in terms of the quality of life. The objectives of this study were to establish a statistical classification model, which can predict the social participation in the old age and provide baseline data for achieving successful aging by using a dataset representing South Korea and combining artificial neural network with QUEST algorithm. This study analyzed the data of 1,864 subjects (829 males and 1,035 females) who were equal to or older than 65 years and completed the 2015 Community Health Survey. The dependent variable was the social activities within the past one month (yes/no). The factors associated with the social participation in the old age were analyzed by using the neural network and QUEST algorithm. Among 1,864 subjects, 1,035 senior citizens (55.5%) did not have social activities during the past one month. The results of established QUEST algorithm classification model revealed that subjective health, age, the frequency of meeting a neighbor, the frequency of meeting a relative, the highest level of education, and walking per week were significant classification variables. Among them, subjective health status was a predictive factor associated with it first. The results of the developed model revealed that social care and institutional measures are needed to promote the social participation of the elderly for achieving the successful aging of the aging society. Â Â