Mining Cluster-based Patterns for Elder Self-care Behavior

The rapid growth of the elderly population has increased the need to support elders in maintaining independent and healthy lifestyles in their homes rather than through more expensive and isolated care facilities. Self-care can improve the competence of elderly participants in managing their own health conditions without leaving home. This main purpose of this study is to understand the self-care behavior of elderly participants in a developed self-care service system that provides self-care service and to analyze the daily self-care activities and health status of elders who live at home alone. To understand elder self-care patterns, log data from actual cases of elder self-care service were collected and analysed by Web usage mining. This study analysed 3391 sessions of 157 elders for the month of March, 2012. First, self-care use cycle, time, function numbers, and the depth and extent (range) of services were statistically analysed. Association rules were then used for data mining to find relationship between these functions of self-care behavior. Second, data from interest-based representation schemes were used to construct elder sessions. The ART2-enhance K-mean algorithm was then used to mine cluster patterns. The analysis results can be used for research in medicine, public health, nursing and psychology and for policy-making in the health care domain.