This study was performed to explore levels of stroke knowledge and identify subgroups with lower levels of stroke knowledge among adults in Korea.

A cross-sectional survey was used and data were collected in 2012. A national sample of 990 Koreans aged 20 to 74 years participated in this study. Knowledge of risk factors, warning signs, and first action for stroke were surveyed using face-to-face interviews. Descriptive statistics and decision tree analysis were performed using SPSS WIN 20.0 and Answer Tree 3.1.

Mean score for stroke risk factor knowledge was 7.7 out of 10. The least recognized risk factor was diabetes and four subgroups with lower levels of knowledge were identified. Score for knowledge of stroke warning signs was 3.6 out of 6. The least recognized warning sign was sudden severe headache and six subgroups with lower levels of knowledge were identified. The first action for stroke was recognized by 65.7 percent of participants and four subgroups with lower levels of knowledge were identified.

Multi-faceted education should be designed to improve stroke knowledge among Korean adults, particularly focusing on subgroups with lower levels of knowledge and less recognition of items in this study.

This descriptive study was done to develop a predictive model of depression in rural elders that will guide prevention and reduction of depression in elders.

A cross-sectional descriptive survey was done using face-to-face private interviews. Participants included in the final analysis were 461 elders (aged≥ 65 years). The questions were on depression, personal and environmental factors, body functions and structures, activity and participation. Decision tree analysis using the SPSS Modeler 14.1 program was applied to build an optimum and significant predictive model to predict depression in rural elders.

From the data analysis, the predictive model for factors related to depression in rural elders presented with 4 path-ways. Predictive factors included exercise capacity, self-esteem, farming, social activity, cognitive function, and gender. The accuracy of the model was 83.7%, error rate 16.3%, sensitivity 63.3%, and specificity 93.6%.

The results of this study can be used as a theoretical basis for developing a systematic knowledge system for nursing and for developing a protocol that prevents depression in elders living in rural areas, thereby contributing to advanced depression prevention for elders.

The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method.

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.

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.

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.