AIM: To investigate the role of artificial neural networks in predicting the presence of thyroid disease in atrophic body gastritis patients. METHODS: A dataset of 29 input variables of 253 atrophic body gastritis patients was applied to artificial neural networks (ANNs) using a data optimisation procedure (standard ANNs, T&T-IS protocol, TWIST protocol). The target variable was the presence of thyroid disease. RESULTS: Standard ANNs obtained a mean accuracy of 64.4% with a sensitivity of 69% and a specificity of 59.8% in recognizing atrophic body gastritis patients with thyroid disease. The optimization procedures (T&T-IS and TWIST protocol) improved the performance of the recognition task yielding a mean accuracy, sensitivity and specificity of 74.7% and 75.8%, 78.8% and 81.8%, and 70.5% and 69.9%, respectively. The increase of sensitivity of the TWIST protocol was statistically significant compared to T&T-IS. CONCLUSION: This study suggests that artificial neural networks may be taken into consideration as a potential clinical decision-support tool for identifying ABG patients at risk for harbouring an unknown thyroid disease and thus requiring diagnostic work-up of their thyroid status. (c) 2008 WJG. All rights reserved.

Artificial neural networks in the recognition of the presence of thyroid disease in patients with atrophic body gastritis / Lahner, Edith; M., Intraligi; M., Buscema; Centanni, Marco; Vannella, Lucy; E., Grossi; Annibale, Bruno. - In: WORLD JOURNAL OF GASTROENTEROLOGY. - ISSN 1007-9327. - 14:4(2008), pp. 563-568. [10.3748/wjg.14.563]

Artificial neural networks in the recognition of the presence of thyroid disease in patients with atrophic body gastritis

LAHNER, EDITH;CENTANNI, Marco;VANNELLA, LUCY;ANNIBALE, Bruno
2008

Abstract

AIM: To investigate the role of artificial neural networks in predicting the presence of thyroid disease in atrophic body gastritis patients. METHODS: A dataset of 29 input variables of 253 atrophic body gastritis patients was applied to artificial neural networks (ANNs) using a data optimisation procedure (standard ANNs, T&T-IS protocol, TWIST protocol). The target variable was the presence of thyroid disease. RESULTS: Standard ANNs obtained a mean accuracy of 64.4% with a sensitivity of 69% and a specificity of 59.8% in recognizing atrophic body gastritis patients with thyroid disease. The optimization procedures (T&T-IS and TWIST protocol) improved the performance of the recognition task yielding a mean accuracy, sensitivity and specificity of 74.7% and 75.8%, 78.8% and 81.8%, and 70.5% and 69.9%, respectively. The increase of sensitivity of the TWIST protocol was statistically significant compared to T&T-IS. CONCLUSION: This study suggests that artificial neural networks may be taken into consideration as a potential clinical decision-support tool for identifying ABG patients at risk for harbouring an unknown thyroid disease and thus requiring diagnostic work-up of their thyroid status. (c) 2008 WJG. All rights reserved.
artificial neural networks; atrophic body gastritis; thyroid disease
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Artificial neural networks in the recognition of the presence of thyroid disease in patients with atrophic body gastritis / Lahner, Edith; M., Intraligi; M., Buscema; Centanni, Marco; Vannella, Lucy; E., Grossi; Annibale, Bruno. - In: WORLD JOURNAL OF GASTROENTEROLOGY. - ISSN 1007-9327. - 14:4(2008), pp. 563-568. [10.3748/wjg.14.563]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/359666
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