[Artificial neural networks applied to study allergic conjunctivitis screening questionnaire].
OBJECTIVE
To evaluate sensibility and specificity of a screening questionnaire with multivariable analysis, compare them and elaborate an artificial neural network for future screenings.


METHODS
Observational, transversal study performed at UNIFESP, with 48 patients with allergic conjunctivitis and 54 children without the disease. Their age ranged between 3 and 14 years and there was no restriction related to gender, systemic allergy or treatment. The questionnaire was applied and multivariable statistical analysis was performed. Finally, an artificial neural network was elaborated.


RESULTS
Mean age was 8.4 years (7-13) and male gender was more frequent (60.7%). Mean score was 10.04 (0-18), and it was higher in the study group (p < 0.001). Allergic diagnosis was increased with the inclusion of the fifth question in 68.8%. Kappa coefficient was low (0.337; p = 0.071) and showed no agreement between diagnosis made by the questionnaire and clinical examination. Only the question number five had good sensitivity (85.4%) and specificity (85.1%). The cutoff point to separate allergic patients was 10 (sensitivity = 77.08% and specificity = 79.63%). The artificial neural network predicted allergic diagnosis in 100% using 7 of the 15 existent items.


CONCLUSIONS
An efficient model was developed using seven questions, in a manner that its application might be easy to large populations.
Goulart
Denise Atique
DA
Setor de Córnea e Doenças Externas, Universidade Federal de São Paulo, São Paulo, SP, Brasil. dagoulart@uol.com.br
Tacla
Milena Atique
MA
Marback
Patrícia Maria Fernandes
PM
Solé
Dirceu
D
Paranhos Junior
Augusto
A
Perez
Homero B
HB
de Freitas
Denise
D
Sato
Elcio Hideo
EH
D000293:Adolescent; D000367:Age Factors; D000465:Algorithms; D001938:Brazil / Q000453:epidemiology; D002648:Child; D002675:Child, Preschool; D003233:Conjunctivitis, Allergic / Q000175:diagnosis* / Q000453:epidemiology; D004812:Epidemiologic Methods; D005260:Female; D006801:Humans; D008297:Male; D008403:Mass Screening; D016571:Neural Networks, Computer; D011795:Surveys and Questionnaires / Q000592:standards*