Introduction. A Noninvasive diagnosis model for digestive diseases is the vital issue for the current clinical research. Our systematic review is aimed at demonstrating diagnosis accuracy between the BP-ANN algorithm and linear regression in digestive disease patients, including their activation function and data structure. Methods. We reported the systematic review according to the PRISMA guidelines. We searched related articles from seven electronic scholarly databases for comparison of the diagnosis accuracy focusing on BP-ANN and linear regression. The characteristics, patient number, input/output marker, diagnosis accuracy, and results/conclusions related to comparison were extracted independently based on inclusion criteria. Results. Nine articles met all the criteria and were enrolled in our review. Of those enrolled articles, the publishing year ranged from 1991 to 2017. The sample size ranged from 42 to 3222 digestive disease patients, and all of the patients showed comparable biomarkers between the BP-ANN algorithm and linear regression. According to our study, 8 literature demonstrated that the BP-ANN model is superior to linear regression in predicting the disease outcome based on AUROC results. One literature reported linear regression to be superior to BP-ANN for the early diagnosis of colorectal cancer. Conclusion. The BP-ANN algorithm and linear regression both had high capacity in fitting the diagnostic model and BP-ANN displayed more prediction accuracy for the noninvasive diagnosis model of digestive diseases. We compared the activation functions and data structure between BP-ANN and linear regression for fitting the diagnosis model, and the data suggested that BP-ANN was a comprehensive recommendation algorithm.

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反向传播人工神经网络模型与线性回归在消化系统疾病患者中的诊断准确性比较：一项实证研究

介绍。消化系统疾病的无创诊断模型是当前临床研究的重要课题。我们的系统评价旨在证明 BP-ANN 算法与消化系统疾病患者线性回归之间的诊断准确性，包括其激活函数和数据结构。方法。我们根据 PRISMA 指南报告了系统评价。我们从七个电子学术数据库中检索了相关文章，以比较侧重于 BP-ANN 和线性回归的诊断准确性。根据纳入标准独立提取与比较相关的特征、患者数量、输入/输出标记、诊断准确性和结果/结论。结果。九篇文章符合所有标准并被纳入我们的审查。在这些入选文章中，发表年份从 1991 年到 2017 年不等。样本量从 42 到 3222 名消化系统疾病患者不等，所有患者在 BP-ANN 算法和线性回归之间显示出可比的生物标志物。根据我们的研究，8 篇文献表明，BP-ANN 模型在基于 AUROC 结果预测疾病结果方面优于线性回归。一篇文献报道线性回归在结直肠癌的早期诊断中优于 BP-ANN。结论。BP-ANN算法和线性回归均具有较高的诊断模型拟合能力，BP-ANN对消化系统疾病无创诊断模型的预测精度更高。我们比较了 BP-ANN 和线性回归之间的激活函数和数据结构来拟合诊断模型，数据表明 BP-ANN 是一种综合推荐算法。