Identifying the medical practice after total hip arthroplasty using an integrated hybrid approach

Abstract

A critical option of total hip arthroplasty (THA) is considered only when tried more conservative treatments but continued to have pain, stiffness, or problems with the function of ones hip. THA plays one of major concerns under the waves of the rapid growth of aging populations and the constrained health care resources in Taiwan. Moreover, prior studies indicated that imbalanced class distribution problems do exist in the constructed classification model and cause seriously negative effects on model performances in the health care industry. Therefore, this study proposes an integrated hybrid approach to provide an alternate method for classifying the quality (e.g., the staying length in hospital) of medical practice with an imbalanced class problem after performing a THA procedure for hip replacement patients and their doctors in the health care industry. The proposed approach is constituted by seven components: expert knowledge, global discretization, imbalanced bootstrap technique, reduct and core methods, rough sets, rule induction, and rule filter. The proposed approach is illustrated in practice by examining an experimental dataset from the National Health Insurance Research Database (NHIRD) in Taiwan. The experimental results reveal that the proposed approach has better performance than the listed methods under evaluation criteria. The output created by the rough set LEM2 algorithm is a comprehensible decision rule set that can be applied in knowledge-based health care services as desired. The analytical results provide useful THA information for both academics and practitioners and these results could be applicable to other diseases or to other countries with similar social and cultural practices.