title: Development of a FPGA based fuzzy neural network system for early diagnosis of critical health condition of a patient.
abstract: The paper describes the design and training of a fuzzy neural network used for early diagnosis of a patient through an FPGA based implementation of a smart instrument. The system employs a fuzzy interface cascaded with a feed-forward neural network. In order to obtain an optimum decision regarding the future pathophysiological state of a patient, the optimal weights of the synapses between the neurons have been determined by using inverse delayed function model of neurons. The neurons that are considered in the proposed network are devoid of self connections instead of commonly used self connected neurons. The current work also find out the optimal number of neurons in the hidden layer for accurate diagnosis as against the available number of CLB in the FPGA. The system has been trained and tested with renal data of patients taken at 10 days interval of time. Applying the methodology, the chance of attainment of critical renal condition of a patient has been predicted with an accuracy of 95.2%, 30 days ahead of actually attaining the critical condition. The system has also been tested for pathophysiological state prediction of patients at multiple time steps ahead and the prediction at the next instant of time stands out to be the most accurate.
authors.affiliation: IC Design and Fabrication Centre, Department of Electronics and Telecommunication Engineering, Jadavpur University, India. shubhajit@juiccentre.res.in
authors.forename: Shubhajit Roy
authors.initials: SR
authors.lastname: Chowdhury
authors.forename: Hiranmay
authors.initials: H
authors.lastname: Saha
mesh_terms: D000465:Algorithms; D003201:Computers; D016638:Critical Illness; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation* / Q000379:methods*; D042241:Early Diagnosis; D005188:False Negative Reactions; D005189:False Positive Reactions; D017143:Fuzzy Logic; D006801:Humans; D016571:Neural Networks, Computer; D011379:Prognosis; D012680:Sensitivity and Specificity