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Development of a FPGA based fuzzy neural network system for early diagnosis of critical health condition of a patient
- Source :
- Computers in Biology and Medicine. 40:190-200
- Publication Year :
- 2010
- Publisher :
- Elsevier BV, 2010.
-
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.
- Subjects :
- Computer science
Critical Illness
Interface (computing)
Health Informatics
Interval (mathematics)
Machine learning
computer.software_genre
Sensitivity and Specificity
Fuzzy logic
Fuzzy Logic
Humans
False Positive Reactions
Diagnosis, Computer-Assisted
Medical diagnosis
Field-programmable gate array
False Negative Reactions
Artificial neural network
Computers
business.industry
Prognosis
Computer Science Applications
Early Diagnosis
Function model
Neural Networks, Computer
State (computer science)
Artificial intelligence
business
computer
Algorithms
Subjects
Details
- ISSN :
- 00104825
- Volume :
- 40
- Database :
- OpenAIRE
- Journal :
- Computers in Biology and Medicine
- Accession number :
- edsair.doi.dedup.....b00dd575458e94f98007cf76350fc639