A three-way efficacy evaluation approach with attribute reduction based on weighted temporal fuzzy rough sets

Abstract

Precise evaluation of clinical efficacy is essential to promote the quality of treatment for patients. Accordingly, some techniques have been used to evaluate or predict the effect of treatment programs, such as statistics, machine learning, and granular computing. In contrast, granular computing can offer flexible, interpretable methods that do not require any prior knowledge to address complex efficacy evaluation problems from a data-driven perspective. Moreover, patients' efficacy information often alternates dynamically between improvement, deterioration, and no change. Granular computing-based methods provide a useful tool for the realization of three-way efficacy classification. Leveraging these advantages, this study attempts to construct a new decision-making approach over the framework of granular computing to deal with a class of efficacy evaluation problems with multi-granularity unbalanced temporal incomplete hybrid decision systems (MGUTIHDSs). To eliminate redundant attributes, we first put forward an attribute reduction method based on weighted temporal fuzzy rough sets. At the same time, several relevant properties are explored. Then, we devise a three-way efficacy evaluation model to objectively complete the personalized evaluation of previous treatment programs. Notwithstanding, it is not feasible to evaluate the efficacy of treatment programs taken at the current time node. To address this issue, a neighborhood-based average similarity prediction method is further developed. Consequently, a three-stage approach including attribute reduction, efficacy evaluation, and efficacy prediction is presented to achieve the efficacy classification of all treatment programs. Finally, the suitability and effectiveness of the approach are demonstrated by a real case study of rheumatoid arthritis. The comparison results indicate that our approach has superior performance, which can provide effective decision support for clinical practice.