Application of rough set classifiers for determining hemodialysis adequacy in ESRD patients

The incidence and the prevalence of end-stage renal disease (ESRD) in Taiwan are the highest in the world. Therefore, hemodialysis (HD) therapy is a major concern and an important challenge due to the shortage of donated organs for transplantation. Previous researchers developed various forecasting models based on statistical methods and artificial intelligence techniques to address the real-world problems of HD therapy that are faced by ESRD patients and their doctors in the healthcare services. Because the performance of these forecasting models is highly dependent on the context and the data used, it would be valuable to develop more suitable methods for applications in this field. This study presents an integrated procedure that is based on rough set classifiers and aims to provide an alternate method for predicting the urea reduction ratio for assessing HD adequacy for ESRD patients and their doctors. The proposed procedure is illustrated in practice by examining a dataset from a specific medical center in Taiwan. The experimental results reveal that the proposed procedure has better accuracy with a low standard deviation than the listed methods. The output created by the rough set LEM2 algorithm is a comprehensible decision rule set that can be applied in knowledge-based healthcare services as desired. The analytical results provide useful information for both academics and practitioners.