Abstrato

Rule based diagnosis system for diabetes

Dilip Kumar Choubey, Sanchita Paul, Vinay Kumar Dhandhenia

In this time of chaotic era, where world is widely affected by diseases such as diabetes, there exists a need for an expert system which can predict diabetes at the very early stages with minimum of fuss and in a time efficient manner. The system should be efficient enough to forecast whether a person is suffering from diabetes or not, with the ability to predict the probability among various types of diabetic types like type-1, type-2, pre-diabetes, and gestational, with which the patient is suffering. The developed system is influenced by various evident factors collected from plethora of sources such as Physicians, books, Internet, medical journals etc. The variety of factors acts as an indicator and serves the basis of rule formation. Rules formations are further engineered with the help of modern techniques like Fuzzy Logic to create an Inference engine. User input is passed to the inference engine, which subsequently produces the output in terms of diabetes type with its probability of occurrence. The proposed expert system is based on a particular demographic and has been further validated on selected patient’s which had produced hundred percent accurate results.