Assessment of Clinical Diagnostic Efficacy of Pulmonary Function Test Based on DBN-SVM of Pediatric Asthma and Cough Variant Asthma.

The diagnosis of asthma depends on the unprejudiced proof of the varying airflow obstruction. The pulmonary function tests are carried out to evaluate the clinical value of different types of respiratory diseases in children or infants. This study is focused on the clinical evaluation of the pulmonary function tests in the diagnosis of pediatric asthma and cough variant asthma. A differential diagnosis method for chronic obstructive pulmonary disease (COPD) and asthma-COPD overlap with complementary diagnostic value is proposed. For the pulmonary function tests, the COPD gene dataset was selected and feature selection was performed using the DBN-SVM scoring method. For analysis and comparison, the differential diagnosis models were built using ROC curves for the accuracy of the deep belief network model and the support vector machine model. The sensitive features associated with COPD and ACO classification using the deep belief network model were found to be in good agreement with known clinical diagnostic strategies. The clinical diagnosis tests for pulmonary pediatric asthma and cough variant asthma were conducted on two groups of children, with both groups containing a basis of comparison. 80 cases of pediatric asthma and cough variant asthma were admitted from 2013 to 2014 and 80 cases of children with a healthy physical examination. The results of the two groups were compared. The results showed that the levels of FEV1, PEF, and FVC were significantly lower (