Application of artificial neural network analysis and decision tree analysis to develop a model for predicting life satisfaction of the elderly in south korea - Byeon, Haewon (International Journal of Engineering and Technology, 2018)
Background/Objectives: This study developed a prediction model with taking into account various factors that could affect the life satisfaction of the elderly in South Korea by using data mining techniques.Methods/Statistical analysis: This study analyzed the data of 2,111 elderly (879 males and 1, 232 females) who were equal to or older than 60 among 7,761 people completed the Seoul Welfare Pane Study 2010. The life satisfaction, a result variable, was classified as ‘satisfactory’, ‘normal’, and ‘dissatisfactory’ based on the question of ‘how are you satisfied with your current life?’The latent factors of the life satisfaction of the elderly were explored by using the neural network. The decision tree model was constructed by using the classification and regression tree (CART) algorithm.Findings: Subjective friendship, subjective health status, subjective family relationship, and the highest level of education were significant classification variables. The most predominant predictive variable was subjective friendship. Moreover, it was predicted that ‘the elderly with good subjective friendship and subjective health’ and ‘the elderly with good subjective friendship, subjective health, and family relationship and whose highest level of education was higher than middle school graduate’ would be groups with high life satisfaction.Improvements/Applications: It is necessary to expand the perceived social network support for promoting the family relationship and friendship as well as the health enhancement in order to improve the life satisfaction of the elderly