A Neuro-Fuzzy Approach to Diagnosis of Neonatal Jaundice
Paper i proceeding, 2006

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

Fuzzy logic

Learning (artificial intelligence)

Fuzzy neural nets

Medical diagnostic computing


Mohammad Sohani

Behrooz Makki

Signaler och system, Kommunikationssystem, informationsteori och antenner, Kommunikationssystem

Nasser Sadati

Kamran Kermani

Ali Riazati

First IEEE conference on Bio-Inspired Models of Network

Vol. 1 1-4


Industriell bioteknik

Datavetenskap (datalogi)