Objective      To explore an intelligent model for diagnosis of common rash and fever illness (RFIs) in children for medical staff in rural area and family members.       Methods        Clinical data of 248 RFIs cases (including 133 males and 115 females with an average age of 4.56) were collected from inpatients in the Southwest Hospital of The Third Military Medical University from Jan 2005 to Nov 2010. Diseases comprised of measles

exanthem subitum

chicken pox

hand-foot-and-mouth disease

scarlet fever

rubella and exanthema. Features of fever

rash

main concomitant symptoms

blood routine and epidemiological data were organized and described

and principal component analysis (PCA) was carried out. PCA combined with back-propagation neural network (BPNN) was used to set up an intelligent diagnosis model for children with common RFIs. The accuracy of the model was further confirmed based on prospective and retrospective analysis.       Results      Thirty-one clinical and epidemiological variables were integrated into 13 principle factors through PCA. These factors were then input to set up a BPNN with a 13-9-7 structure. When the model was used for children with RFIs

the average accuracy rate of retrospective diagnosis reached 99.53%

and the average accuracy rate of predictive diagnosis was 92.86%.       Conclusion      BPNN diagnosis model on the basis of clinical samples can be applied for an accurate diagnosis of common RFIs in children

and has an significant application prospect.