Pleural Tuberculosis Diagnosis Based On Artificial Neural Networks Models

Faria, João F.G.; Seixas, J. M.; Oliveira, João Baptista de; Filho, Souza; Orjuela, Alvaro D.; Filipe, Andre; Marcondes, Vieira; Kritski, Afrânio Lineu; Michelly, Isabel; Silva, F.; Trajman, Anete

doi:10.21528/cbic2011-13.3

Cited by 2 publications

(3 citation statements)

References 18 publications

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“…It is possible to see that the results in pre-diagnostic applications offer a difference of 1.05 % for the two approaches, showing that just pre-test information can be useful. This could be explaining by the use of variables most important in the detection task, characteristics that have been studied in previously works [9] [13],where the anamnesis variables are enough to detect pTB with results close to 90% in sensitivity.…”

Section: Discussionmentioning

confidence: 96%

“…Table I summarizes both approaches. Each patient's record was consulted to gather information on 11 variables. The two added variables correspond to a pleural tissue culture, and a histopathological test using a biopsy of the pleura [9].The results of these tests were not used because it sought a diagnosis that is little invasive and painful to the patient. Additional to each 11 variables, the final diagnostic is stored in the database.…”

Section: Amentioning

confidence: 99%

“…An example of this can be sought in the pulmonary tuberculosis problem, where Multi-Layer Perceptron networks (MLP) were used [7] [8]. In pleural tuberculosis (pTB) also these techniques have shown relevant results using MLP neural networks [9] [10].…”

Section: Introductionmentioning

confidence: 99%

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SOM Neural Networks as a Tool in Pleural Tuberculosis Diagnostic

Orjuela-Cañón¹,

Seixas²,

Trajman³

2016

Anais Do 11. Congresso Brasileiro De Inteligência Computacional

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Diagnostic tasks in a contagious disease as pleural tuberculosis is essential to send the patients to a correct treatment to cure the ailment. Tools for supporting this task are still a challenge because different variables are necessary, including invasive standard tests. In the present work Self Organizing Maps were used for clustering of patients of pleural tuberculosis in three risk groups. The first approach employs just anamnesis variables and a second methodology uses additional information about some classical result tests. The last approach exhibits best results compared to the approach using anamneses variables

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Section: Discussionmentioning

confidence: 96%

Section: Amentioning

confidence: 99%

See 1 more Smart Citation

SOM Neural Networks as a Tool in Pleural Tuberculosis Diagnostic

Orjuela-Cañón¹,

Seixas²,

Trajman³

2016

Anais Do 11. Congresso Brasileiro De Inteligência Computacional

Self Cite

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Fuzzy-ART neural networks for triage in pleural tuberculosis

Orjuela-Cañón

Seixas

2013

2013 Pan American Health Care Exchanges (PAHCE)

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Triage in a contagious disease as pleural tuberculosis is essential to send the patients to a correct treatment to cure the ailment. These tools are still a challenge because some invasive standard tests are necessary to detect it. The present work shows two models to do a clustering and classify patients of pleural tuberculosis in three risk groups. This clustering uses Fuzzy-Art neural networks to find these groups in the data. First approach employs just anamneses variables and second methodology uses additional information about some classical result tests. The last approach exhibits best results compared to the approach using anamneses variables. Results for sensitivity are similar in two approaches, presenting 93.75% in the anamneses case and 96.87% using more variables.

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Pleural Tuberculosis Diagnosis Based On Artificial Neural Networks Models

Cited by 2 publications

References 18 publications

SOM Neural Networks as a Tool in Pleural Tuberculosis Diagnostic

SOM Neural Networks as a Tool in Pleural Tuberculosis Diagnostic

Fuzzy-ART neural networks for triage in pleural tuberculosis

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