Hybride neuro-fuzzy expert system for assessing diabetes risk

There is an increasing number of diabetic diseases in the population. Early diagnosis increases possibility of healing and decreases healing expenses. This paper is focused on modeling of an expert system for assessing diabetes risk using artificial intelligence methods. The model is hierarchical with neuro-fuzzy blocks and voting on the output. Model optimization (learning) is done using the data of real patients acquired in a one public health institution. The expert system is implemented in the Matlab/Simulink environment. Validation of the system shows high sensitivity of 100% which is important for early diagnoses. Specificity is lower - only 90% which means that some patients are sent to further diagnoses although they are healthy. Results indicate that this modeling approach is applicable on assessing diabetes risk. The medical domain knowledge and experience contained in the real data is successfully transferred in the solution model - the proposed expert system. Validation of the proposed expert system indicates that such a system can be used as an auxiliary expert for early diagnoses of diabetes and improve the quality of health systems with lower diagnostic expenses.