Comparison of Diagnosis Accuracy between a Backpropagation Artificial Neural Network Model and Linear Regression in Digestive Disease Patients: an Empirical Research
单位：[1]Clinical Epidemiology and Evidence-Based Medical Center, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing 100050, China医技科室北京市临床医学研究所国家消化中心（方法学平台）首都医科大学附属北京友谊医院[2]School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
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.
基金：
National Nature Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [91846303]; Beijing Municipal Administration of Hospitals Incubating Program [PX2018071]; Research Foundation of Beijing Friendship Hospital, Capital Medical University [yyqdkt2015-15]
第一作者单位：[1]Clinical Epidemiology and Evidence-Based Medical Center, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing 100050, China
通讯作者：
推荐引用方式(GB/T 7714)：
Wei Wei,Xu Yang.Comparison of Diagnosis Accuracy between a Backpropagation Artificial Neural Network Model and Linear Regression in Digestive Disease Patients: an Empirical Research[J].COMPUTATIONAL and MATHEMATICAL METHODS in MEDICINE.2021,2021:doi:10.1155/2021/6662779.
APA：
Wei Wei&Xu Yang.(2021).Comparison of Diagnosis Accuracy between a Backpropagation Artificial Neural Network Model and Linear Regression in Digestive Disease Patients: an Empirical Research.COMPUTATIONAL and MATHEMATICAL METHODS in MEDICINE,2021,
MLA：
Wei Wei,et al."Comparison of Diagnosis Accuracy between a Backpropagation Artificial Neural Network Model and Linear Regression in Digestive Disease Patients: an Empirical Research".COMPUTATIONAL and MATHEMATICAL METHODS in MEDICINE 2021.(2021)
