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A Neuro-Fuzzy Approach to Diagnosis of Neonatal Jaundice

Mohammad Sohani ; Behrooz Makki (Institutionen för signaler och system, Kommunikationssystem) ; Nasser Sadati ; Kamran Kermani ; Ali Riazati
First IEEE conference on Bio-Inspired Models of Network Vol. 1 (2006), 1, p. 1-4.
[Konferensbidrag, refereegranskat]

This paper presents an approach that integrates clinical methods with neuro-fuzzy system in order to diagnose neonatal jaundice in newborns. First, a fuzzy logic system designed with medical rules to model the uncertainty that exists in medical diagnosis. Then a fuzzy neural network with an evolutionary learning helps the system to learn the new data gained from the patient and to help the fuzzy system to update itself in an online manner. By combining the aforementioned systems, the proposed approach can help physicians to diagnose jaundice with low risk cost associated with this disease

Nyckelord: Fuzzy logic; Fuzzy neural nets; Learning (artificial intelligence); Medical diagnostic computing

Denna post skapades 2009-02-26. Senast ändrad 2017-10-03.
CPL Pubid: 90441


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Institutioner (Chalmers)

Institutionen för signaler och system, Kommunikationssystem (1900-2017)


Datavetenskap (datalogi)
Industriell bioteknik

Chalmers infrastruktur