Fuzzy-based Diabetes Decision Support System

In the medical field, where uncertainty and ambiguity are prevalent and patient numbers are continuously rising, there is a pressing need for an effective control system that can reduce vagueness and uncertainty for doctors while enhancing diagnostic accuracy. Medical tests play a crucial role for doctors in detecting diseases and determining their stages. In this research, the authors introduce a robust and efficient system proficient of handling various diabetic test inputs and delivering extremely reliable outputs for diagnosing health conditions and making decisions for treatment. Fuzzy logic has been selected for its simplicity in design and implementation, as well as its outstanding efficiency in handling uncertain and imprecise data. The fuzzy system uses data from medical test report as input parameters, and simulations are performed utilizing the MATLAB R2023b toolkit, with implementation completed in visual studio 2022 utilizing C# programming. The simulation outcomes closely match the designed values, with percentage errors falling within an adequate (< 9%) range, indicating the reliability of the model in improving diagnostic accuracy and treatment decision-making in diabetes management. DOI: 10.61416/ceai.v27i1.9406