Clinical signs of pneumonia in children: association with and prediction of diagnosis by fuzzy sets theory

Brazilian Journal of Medical and Biological Research

J.c.r. Pereira²  P.a. Tonelli¹  L.c. Barros¹  N.r.s. Ortega¹ 

[1] ,Universidade de São Paulo Faculdade de Saúde Pública Hospital das Clínicas

关键词: Epidemiologic methods;    Stochastic models;    Fuzzy models;    Clinical signs;    Diagnosis;    Data analysis;   

DOI  :  10.1590/S0100-879X2004000500012

来源: SciELO

【 摘 要 】

The present study compares the performance of stochastic and fuzzy models for the analysis of the relationship between clinical signs and diagnosis. Data obtained for 153 children concerning diagnosis (pneumonia, other non-pneumonia diseases, absence of disease) and seven clinical signs were divided into two samples, one for analysis and other for validation. The former was used to derive relations by multi-discriminant analysis (MDA) and by fuzzy max-min compositions (fuzzy), and the latter was used to assess the predictions drawn from each type of relation. MDA and fuzzy were closely similar in terms of prediction, with correct allocation of 75.7 to 78.3% of patients in the validation sample, and displaying only a single instance of disagreement: a patient with low level of toxemia was mistaken as not diseased by MDA and correctly taken as somehow ill by fuzzy. Concerning relations, each method provided different information, each revealing different aspects of the relations between clinical signs and diagnoses. Both methods agreed on pointing X-ray, dyspnea, and auscultation as better related with pneumonia, but only fuzzy was able to detect relations of heart rate, body temperature, toxemia and respiratory rate with pneumonia. Moreover, only fuzzy was able to detect a relationship between heart rate and absence of disease, which allowed the detection of six malnourished children whose diagnoses as healthy are, indeed, disputable. The conclusion is that even though fuzzy sets theory might not improve prediction, it certainly does enhance clinical knowledge since it detects relationships not visible to stochastic models.

【 授权许可】

CC BY   
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