摘要
AIM: To investigate the role of artifi cial 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 artifi cial 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 specifi city 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 specifi city 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 signifi cant 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.
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.
基金
funds from MIUR 2005 (Italian Ministry for University and Research) and University Sapienza Roma
关键词
萎缩性胃炎
甲状腺疾病
人造神经网络
症状
Atrophic body gastritis
Thyroid disease
Artificial neural networks
