An empirical study of a hybrid imbalanced-class DT-RST classification procedure to elucidate therapeutic effects in uremia patients

The high prevalence and incidence of severe renal diseases exhaust constrained medical resources for the treatment of uremia patients. In addition, the problem of imbalanced-class data distributions induces negative effects on classifier learning algorithms. Hemodialysis is the most common treatment for uremia diseases due to the limited supply of donated organs available for transplantation. This study focused on assessing the adequacy of hemodialysis. The lack of available information represents the primary obstacle limiting the evaluation of adequacy, namely: (1) the imbalanced-class problem in a given dataset, (2) obeying mathematical distributions for a given dataset, (3) a lack of effective methods for identifying determinant attributes, and (4) developing effective decision rules to explain a given dataset. To address these issues for determining the therapeutic effects of hemodialysis in uremia patients, this study proposes a hybrid imbalanced-class decision tree-rough set model to integrate the knowledge of expert physicians, a feature selection method, imbalanced sampling techniques, a rough set classifier, and a rule filter. The method was assessed by examining the medical records of uremia patients from a medical center in Taiwan. The proposed method yields better performance compared to previously reported methods according to the evaluation criteria.