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- W2365604865 abstract "Objective To establish a model based on artificial neural network for diagnosis of smear negative pulmonary tuberculosis and evaluating its performance. Methods All original data was randomly divided into training sample and testing sample.The training samples were used to screen out significant single parameter and develop the diagnostic model of smear negative pulmonary tuberculosis based on artificial neural network.The area under the receiver operator characteristic curve,sensitivity and specificity were used to evaluate the model's performance in both training and testing samples. Results The structure of artificial neural network is(29-9-1)-BP.When the model was applied to the training sample and the testing sample,the area under the receiver operator characteristic curve,accuracy,sensitivity and specificity were 0.984±0.004 and 0.955±0.018,91.07% and 93.10%,90.91% and 88.89%,91.30% and 100% respectively. Conclusion Artificial neural network model is capable of diagnosing smear negative pulmonary tuberculosis and it can be used as a tool in routine diagnostic practice." @default.
- W2365604865 created "2016-06-24" @default.
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- W2365604865 date "2007-01-01" @default.
- W2365604865 modified "2023-09-27" @default.
- W2365604865 title "Establishment of diagnostic model of smear negative pulmonary tuberculosis based on artificial neural network." @default.
- W2365604865 hasPublicationYear "2007" @default.
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