FPGA Based Diabetic Patient Health Monitoring Using Fuzzy Neural Network

This is a FPGA based system and this system employs a fuzzy interface cascaded with a feed-forward neural network in order to obtain an optimum decision regarding the future pathology physiological state of a patient. The neurons that are considered in the proposed network are devoid of self-connections instead of commonly used self-connected neurons. Applying the methodology, the chance of forecasting of critical diabetic condition of a patient can be predicted accurately, 30 days ahead of actually attaining the critical condition. The fuzzy interface discussed here performs fuzzification of patient data. The data from the patient such as height or weight data cannot always be trusted as they are subjected to the quality and accuracy of measuring units and the skill of the technician. Moreover, based on a single data, it would be highly uncertain to make an accurate decision about the future physiological state of the patient. So the patient data have been fuzzified with the objective of transformation of periodic measures into likelihoods that the body mass index, blood glucose, urea, creatinine, systolic and diastolic blood pressure of the patient is high, low or moderate.