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首页> 外文期刊>World Journal of Gastroenterology >Possible contribution of artificial neural networks and linear discriminant analysis in recognition of patients with suspected atrophic body gastritis.
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Possible contribution of artificial neural networks and linear discriminant analysis in recognition of patients with suspected atrophic body gastritis.

机译：人工神经网络和线性判别分析在识别可疑萎缩性胃炎患者中的可能贡献。

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AIM: To investigate whether ANNs and LDA could recognize patients with ABG in a database, containing only clinical and biochemical variables, of a pool of patients with and without ABG, by selecting the most predictive variables and by reducing input data to the minimum.METHODS: Data was collected from 350 consecutive outpatients (263 with ABG, 87 with non-atrophic gastritis and/or celiac disease [controls]). Structured questionnaires with 22 items (anagraphic, anamnestic, clinical, and biochemical data) were filled out for each patient. All patients underwent gastroscopy with biopsies. ANNs and LDA were applied to recognize patients with ABG. Experiment 1: random selection on 37 variables, experiment 2: optimization process on 30 variables, experiment 3: input data reduction on 8 variables, experiment 4: use of only clinical input data on 5 variables, and experiment 5: use of only serological variables.RESULTS: In experiment 1, overall accuracies of ANNs and LDA were 96.6% and 94.6%, respectively, forpredicting patients with ABG. In experiment 2, ANNs and LDA reached an overall accuracy of 98.8% and 96.8%, respectively. In experiment 3, overall accuracy of ANNs was 98.4%. In experiment 4, overall accuracies of ANNs and LDA were, respectively, 91.3% and 88.6%. In experiment 5, overall accuracies of ANNs and LDA were, respectively, 97.7% and 94.5%.CONCLUSION: This preliminary study suggests that advanced statistical methods, not only ANNs, but also LDA, may contribute to better address bioptic sampling during gastroscopy in a subset of patients in whom ABG may be suspected on the basis of aspecific gastrointestinal symptoms or non-digestive disorders.
机译：目的：通过选择预测性最强的变量并将输入数据减至最少，以调查ANN和LDA是否能识别数据库中仅包含临床和生化变量的ABG患者，该数据库仅包含临床和生化变量。 ：从350名连续的门诊患者（263名ABG患者，87名非萎缩性胃炎和/或腹腔疾病[对照]）中收集了数据。为每位患者填写了结构化的问卷，其中包含22项内容（分析，回忆，临床和生化数据）。所有患者均接受胃镜活检。人工神经网络和LDA用于识别ABG患者。实验1：对37个变量进行随机选择；实验2：对30个变量进行优化处理；实验3：对8个变量进行输入数据缩减；实验4：仅对5个变量使用临床输入数据；实验5：仅对血清变量使用结果：在实验1中，ANNs和LDA的总体准确度分别为96.6％和94.6％，可预测ABG患者。在实验2中，人工神经网络和LDA的总体准确度分别达到98.8％和96.8％。在实验3中，人工神经网络的整体准确性为98.4％。在实验4中，人工神经网络和LDA的总体准确度分别为91.3％和88.6％。在实验5中，人工神经网络和LDA的总体准确度分别为97.7％和94.5％。结论：这项初步研究表明，先进的统计方法，不仅人工神经网络，而且LDA都可能有助于更好地处理胃镜检查中的活检采样。可能因特殊的胃肠道症状或非消化系统疾病而被怀疑患有ABG的患者亚群。

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