Abstract：Objective To provide diagnostic clue for the investigation and laboratory examination in outbreak of common respiratory infectious diseases using a computer-aided classification model.Methods The variables were extracted from medical literature, case data of infectious diseases, reports of outbreaks such as symptoms and signs, abnormal lab test results, epidemiologic features, the incidence rates of the infectious diseases. Then a classification model was constructed using Naive Bayesian classifier and SAS 9.1.3 Data from eight historical outbreaks of respiratory infectious diseases were used to test the model.Results Among eight outbreaks, the discriminate probability of diagnosing a disease correctly by ranking it first on the output lists of the model was 53.85%. The sensitivity was 53.85%, and specificity was 100.00%,and +LR was from 5.73 to ∞. The discriminant probability of diagnosing a disease correctly by ranking it within the three most probable diseases on these lists was 98.34%. The sensitivity was 98.34% and the specificity was 82.14%,and +LR was from 1.26 to ∞.Conclusion A Bayesian classification model could be applied to classification and discriminant of common respiratory infectious diseases, and could improve the ability for early diagnosis of the outbreak caused by respiratory infectious diseases.
王臻, 李傅冬, 刘碧瑶, 戚小华. 基于贝叶斯定理的常见呼吸道传染病分类判别模型研究[J]. 预防医学, 2016, 28(9): 870-873.
WANG Zhen, LI Fu-dong, LIU Bi-yao, QI Xiao-hua. A study on Bayesian classification model for common respiratory infectious diseases. Preventive Medicine, 2016, 28(9): 870-873.
