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. 2008 Jan 28;14(4):563-8.
doi: 10.3748/wjg.14.563.

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

Edith Lahner  1 , Marco Intraligi, Massimo Buscema, Marco Centanni, Lucy Vannella, Enzo Grossi, Bruno Annibale
Affiliations

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

Edith Lahner et al. World J Gastroenterol. .

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.

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Figures

Figure 1
Figure 1
Application protocols of artificial neural networks and linear discriminant analyses.
See this image and copyright information in PMC

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