Discovering medical resource utilization in total knee arthroplasty (TKA) using rule-based method

TKA is a highly effective means of treating (advanced knee arthritis) degenerative joint disease. Previous studies have demonstrated that a high surgical volume for total joint arthroplasty reduces morbidity and improved economic outcome, these methods for themselves are fraught with complexity, uncertainty and non-linear problem in terms of medical datasets may be unable to more accurately finding important information. As medical datasets often include a large number of features (attributes), some of which are irrelevant, and therefore it cannot intuitively understand the corresponding to main factors which affecting the resource utilizations of healthcare. In order to solve the problems mentioned above, this study employs specialist advice to filter relevant cases (records) and proposed an integrated five features selection methods to select the important features. Based on rough set theory (RST), the rules are extracted and compared with other methods in terms of accuracy. The contributions contain: (1) data screening based on specialist opinions, (2) two stage feature selection by analysis of variance (ANOVA) and proposed an integrated feature selection approach (IFSA), and (3) data discretization and rule generation by RST. The proposed model is verified by using three datasets for comparison accuracy. The results can provide a valuable reference for National Health Insurance Bureau (NHI) in establishing the TKA standard.