A study on Bayesian classification model for common respiratory infectious diseases / 预防医学
Journal of Preventive Medicine
;
(12): 870-873, 2016.
Artigo
em Chinês
| WPRIM
| ID: wpr-792537
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 1 00.00%, 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 82.1 4%,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.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Tipo de estudo:
Estudo prognóstico
/
Estudo de rastreamento
Idioma:
Chinês
Revista:
Journal of Preventive Medicine
Ano de publicação:
2016
Tipo de documento:
Artigo
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