Novel three-way decision model in medical diagnosis based on inexact reasoning

Abstract

According to the clinical symptoms, a patient exhibits and medical domain knowledge, determining the type of disease the patient has is essential for medical diagnosis. Uncertainty is the nature of medical diagnosis. The intuitionistic fuzzy set (IFS) is one of the effective tools to deal with uncertainty problems. In recent years, IFS has been widely used to deal with medical diagnosis problems. The existing mainstream approach is to rank the underlying diseases and then assign the first-ranked disease to the patient. These methods cannot deal with situations where patients have multiple diseases at the same time or no disease at all. In addition, these methods may generate unreasonable diagnostic results in some cases. Consider clinical symptoms as evidence that a patient has or does not have the disease. With the increase in clinical symptoms, physicians will increase their belief or disbelief that the patient has the disease. Therefore, based on the method of inexact reasoning, this paper proposed an inexact reasoning model in medical diagnosis, namely IRM-MD, which can rank the underlying diseases and avoid generating unreasonable diagnostic results. Furthermore, a three-way decision model for medical diagnosis based on IRM-MD, namely 3WDM-IRM, is proposed. This model can effectively deal with cases where patients have multiple conditions at the same time or none at all, and prevent missed diagnoses and misdiagnoses. Finally, the numerical experiment results show that (a)the results generated by the IRM-MD model are similar to those of other existing models, and (b)the 3WDM-IRM model can effectively identify whether a patient has a disease or not, and identify multiple diseases that the patient has at the same time.