The level of unmet needs of persons with dementia and their family caregivers must be considered in the development of support programs focused on improving caregiving satisfaction.
Purpose: The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method. Methods: A large dataset from the 2008 Korean Elderly Survey was used and data of 14,970 elderly people were analyzed. Target variable was depression and 53 input variables were general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment. Data were analyzed by decision tree analysis, a data mining technique using SPSS Window 19.0 and Clementine 12.0 programs. Results: The decision trees were classified into five different rules to define the characteristics of older adults with depression. Classification & Regression Tree (C&RT) showed the best prediction with an accuracy of 80.81% among data mining models. Factors in the rules were life satisfaction, nutritional status, daily activity difficulty due to pain, functional limitation for basic or instrumental daily activities, number of chronic diseases and daily activity difficulty due to disease. Conclusion: The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to these individual characteristics.
Background: It has been proven that an individuals health behavior is determined through a series of processes. This study aimed to assess the stages of adoption of breast cancer screening, and to identify the factors relating to progress through these stages. Materials and Methods: There were 202 female participants aged 20-59 years who were living in Chungbuk, South Korea. They were informed of the study purpose and agreed to participate. Data were collected from October 2010 to January 2011 by assessing the breast cancer screening stage, health beliefs, socio-demographic factors, and other facilitating factors. The participant current stage of adoption of breast cancer screening was classified using the Precaution Adoption Process Model (PAPM), and the various PAPM stages were compared with each other to identify factors likely to determine progress between stages. The data were analyzed using the χ2-test, ANOVA, Duncan test, and multiple logistic regression. Results:Approximately half of all participants were not on-schedule for breast self-examination and mammography (unaware, 9.4% and 11.4%, unengaged, 8.4% and 5.0%, undecided, 20.3% and 17.8%, decided not to act, 1.5% and 1.0%, decided to act, 13.4% and 15.3%, respectively). The factors likely to determine the progress from one stage to another were age, marital status, exposure to media information about breast cancer, self-efficacy, and perceived severity. Conclusions: These results suggest that it is necessary to develop a tailored message for breast cancer screening behavior.
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