A Case Study of Using Classifi cation and Regression Tree and LRFM Model in A Pediatric Dental Clinic
Shih‐Yen Lin, Jo-Ting Wei, Hsin‐Hung Wu · 2011
A case study in a pediatric dental clinic was presented. The data were transformed into LRFM (Length, Recency, Frequency, and Monetary) format with fixed M covered by National Health Insurance program in Taiwan, where the data were categorized into 1 to 5 for L, R, and F variables. Later, gender was classified into two types, and age was grouped into four categories. The target in this study was frequency, while L, R, gender, and age were the input variables when classification and regression tree was performed. The overall accuracy is about 60% but the prediction accuracies for lost patients and very important patients were very effective. Therefore, the dental clinic can pay much attention to those who might be considered as very important patients.