Design, Simulation and Testing of an Optimized Fuzzy Neural Network for Early Criticality Diagnosis

Current trends in the application of computational intelligent systems for cognitive health care applications have motivated the design of neuro-fuzzy systems that can signal the approaching critical condition of a patient at an early stage. This article proposes the design of an optimized fuzzy neural network that can predict the future patho-physiological state of a patient based on the previous data. The membership functions used for fuzzying the patient data is sigmoidal and has been suitably optimized for correctly diagnosing the patho-physiological trend of the patient. Using the trained network, the chance of approaching critical health condition of a patient has been predicted with confidence. The system has been suitably tested with height, weight, glucose, urea, creatinine, systolic blood pressure and diastolic blood pressure data of patients taken at 10 days interval of time. Finally we study the effect of slight variation in the center and width of the sigmoids, used for fuzzification, on the reliability and the time instants at which we can predict the criticality.