Mining the optimal clustering of peoples characteristics of health care choices
In Asian countries, there has been a multi-choice healthcare environment for many years. In Taiwan, peoples multiple health care seeking behavior has resulted in much heavier financial burden of National Health Insurance Program (NHIP) in recent years: investigating the characteristics of people who use multiple health care resources has gained increasing importance for health authorities. In this study, we investigated the socioeconomic and demographic characteristics which underlined peoples choice of health care by using a population representative database. A novel methodology which incorporated k-means cluster analysis with v-fold cross-validation into Multiple Correspondence Analysis (MCA) is proposed. This novel methodology can help us to find the optimal attribute clustering of multiple health care utilization. By using this methodology, researchers not only can avoid the ambiguities of identifying clusters resulted from the traditional hierarchical cluster analysis (HCA), but also can provide more solid and evidence-based analysis for health policy making.
health care k-means cluster analysis v-fold cross-validation multiple correspondence analysis