Rough set theory-based multi-class decision-making framework for cost-effective treatment

This paper provides a novel formulation of multiclass decision-making with the help of rough set theory. Rough set theory approximates a concept by the three regions, namely positive, negative, and boundary regions. The three regions enable us to derive three types of decisions, namely acceptance, rejection, and deferment. The deferment decision allows us to examine suspicious objects further and reduces the chance of misclassification. Different types of misclassification errors are treated separately based on the notation of loss function from Bayesian decision theory. In our cost-sensitive classification approach, the costs caused by different kinds of error are not assumed to be equal. The main object of this paper is to provide a direction for cost-effective treatment for a patient suspected to be affected by a significant disease. A numerical example of cost-effective treatment for a patient suspected to be affected by a significant disease demonstrates the practicability and efficacy of the developed idea in real-life applications.