Discovering medical quality of total hip arthroplasty by rough set classifier with imbalanced class

The incidence of THA (total hip arthroplasty) will rise with an aging population and improvements in surgery, a feasible alternative in health care can effectively increase medical quality. The reason of a hip joint replaced is to relieve severe arthritis pain that is limiting your activities. Hip joint replacement is usually done in people age 60 and older. Younger people who have a hip replaced may put extra stress on the artificial hip. This paper uses a serious data screening function by experts to reduce data dimension after data collection from the National Health Insurance database. The proposed model also adopts an imbalanced sampling method to solve class imbalance problem, and utilizes rough set theory to find out core attributes (selected 7 features). Based on the core attributes, the extracted rules can be comprehensive for the rules of medical quality. In verification, THA dataset is taken as case study; the performance of the proposed model is verified and compared with other data-mining methods under various criteria. Furthermore, the performance of the proposed model is identified as winning the listing methods, as well as using hybrid-sampling can increase the far true-positive rate (minority class). The results show that the proposed model is efficient; the performance is superior to the listing methods under the listing criteria. And the generated decision rules and core attributes could find more managerial implication. Moreover, the result can provide stakeholders with useful THA information to help make decision.