标题：Evaluating Factors for Predicting the Life Dissatisfaction of South Korean Elderly using Soft Margin Support Vector Machine based on Communication Frequency, Social Network Health Behavior and Depression
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：9
页码：392-398
出版社：Science and Information Society (SAI)
摘要：Since health and the quality of life are caused not
by a single factor but by the interaction of multiple factors, it is
necessary to develop a model that can predict the quality of life
using multiple risk factors rather than to identify individual risk
factors. This study aimed to develop a model predicting the
quality of life based on C-SVM using big data and provide
baseline data for a successful old age. This study selected 2,420
elderly (1,110 men, 1,310 women) who were 65 years or older and
completed the Seoul Statistics Survey. The quality of life
satisfaction, a binary outcome variable (satisfied or dissatisfied),
was evaluated based on a self-report questionnaire. This study
performed a Gauss function among the SVM algorithms. To
verify the predictive power of the developed model, this study
compared the Gauss function with the linear algorithm,
polynomial algorithm, and sigmoid algorithm. Additionally, CSVM
and Nu-SVM were applied to four kernel algorithm types
to create eight types, and prediction accuracies of the eight SVM
types were estimated and compared. Among 2,420 subjects, 483
elderly (19.9%) were not satisfied with their current lives. The
final prediction accuracy of this SVM using 625 support vectors
was 92.63%. The results showed that the difference between CSVM
and Nu-SVM was negligible in the models for predicting
the satisfaction of life in old age while the Gaussian kernel had
the highest accuracy and the sigmoid kernel had the lowest
accuracy. Based on the prediction model of this study, it is
required to manage local communities systematically to enhance
the quality of life in old age.
关键词：C-SVM; communication frequency; life
satisfaction; social network; quality of life
