Abstract:

Objective This study on mathematical model of comprehensive clinicl diagnosis of tuberculosis (TB) based on big data is aimed to improve the clinical diagnosis of tuberculosis. Methods A total of 345 patients from the Chronic Disease Prevention and Control Hospital of Qingyuan City between January 2016 and December 2017 were selected using stratified random sampling method.They were divided into TB group (n=198, initial therapy for active TB) and no-TB (n=147, including old TB).Results of X-ray,PPD, WBC, serum albumin, sputum smear, sputum culture, GeneXpert MTB/RIF test and T-SPOT.TB, clinical symptoms and medical history were analyzed using logistic regression model and discriminant analysis.Results After detecting sexual (χ²=6.047,P=0.014;OR (95%CI)=0.410 (0.201-0.834)), TB history (χ²=29.273,P=0.000; OR (95%CI)=0.086 (0.036-0.210)), WBC (χ²=10.266, P=0.001; OR (95%CI)=0.771 (0.657-0.904)), GeneXpert MTB/RIF (χ²=18.633, P=0.000; OR (95%CI)=7.280 (2.956-17.928)), sputum culture (χ²=10.400, P=0.001; OR (95%CI)=10.021 (2.469-40.664)), T-SPOT.TB (χ²=23.669, P=0.000; OR (95%CI)=5.769 (2.848-11.688)), PPD (χ²=6.462, P=0.011; OR (95%CI)=1.503 (1.098-2.057))and weight loss (χ²=7.807, P=0.005; OR (95%CI)=3.335 (1.433-7.761)), it was found that all ORs of GeneXpert MTB/RIF,sputum culture, T-SPOT.TB and weight loss were above 2, which was significantly help to diagnosis of TB. Function model of TB diagnosis is useful, the accuracy in differential diagnosis of no-TB was 83.67% (123/147), and the accuracy in TB diagnosis was 79.29% (157/198). Conclusion Function model of TB diagnosis in this study is of high accuracy in differential diagnosis, it help to clinical diagnosis of TB and could reduce the misdiagnosis rate.

Key words: Tuberculosis, pulmonary, Computer communication networks, Diagnosis, differential, Models, statistical, Data interpretation, statistical, Evaluation studies