Diabetes diagnosis expert system by using Belief Rule Base with evidential reasoning

Diabetes is a knotty disease and very common in the world. It can affect almost every organ of the body. The diagnosis of diabetes is determining from some medical and clinical data associate with diabetes. However, all the data are associated various types of uncertainty, which can cause for time delay, inaccuracy of the diagnosis. A system for automated medical diagnosis would enhance the accuracy of the diagnosis and reduce the cost effects. This paper presents an effective approach for diagnosing diabetes using Belief Rule Base (BRB) with evidential reasoning, which can handle the errors and uncertainties. This paper used the medical and clinical real data to develop and test this system. It has been observed that, this system provides user interactive and reliable results of diabetes diagnosis in percentage.