scholarly journals Multivariable Logistic Regression And Back Propagation Artificial Neural Network To Predict Diabetic Retinopathy

2019 ◽  
Vol Volume 12 ◽  
pp. 1943-1951 ◽  
Author(s):  
Litong Yao ◽  
Yifan Zhong ◽  
Jingyang Wu ◽  
Guisen Zhang ◽  
Lei Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Diabetic Retinopathy ◽  
Back Propagation ◽  
Multivariable Logistic Regression ◽  
Artificial Neural

scholarly journals Comparison of Artificial Neural Networks and Logistic Regression Analysis in Pregnancy Prediction Using the In Vitro Fertilization Treatment

2013 ◽  
Vol 35 (1) ◽  
pp. 39-48 ◽  
Author(s):  
Robert Milewski ◽  
Anna Justyna Milewska ◽  
Teresa Więsak ◽  
Allen Morgan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Multivariable Logistic Regression Analysis ◽  
Vitro Fertilization ◽  
Multivariable Logistic Regression ◽  
Artificial Neural

Abstract Infertility is recognized as a major problem of modern society. Assisted Reproductive Technology (ART) is the one of many available treatment options to cure infertility. However, the efficiency of the ART treatment is still inadequate. Therefore, the procedure’s quality is constantly improving and there is a need to determine statistical predictors as well as contributing factors to the successful treatment. There is a concern over the application of adequate statistical analysis to clinical data: should classic statistical methods be used or would it be more appropriate to apply advanced data mining technologies? By comparing two statistical models, Multivariable Logistic Regression analysis and Artificial Neural Network it has been demonstrated that Multivariable Logistic Regression analysis is more suitable for theoretical interest but the Artificial Neural Network method is more useful in clinical prediction.

Test on Flood Prediction-Model Using Artificial Neural Network for ShiiLiAn Hydrologic Station on MinChiang,China

2010 ◽  
Vol 39 ◽  
pp. 555-561 ◽  
Author(s):  
Qing Hua Luan ◽  
Yao Cheng ◽  
Zha Xin Ima
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flood Control ◽  
Back Propagation ◽  
Forecast Model ◽  
Flood Forecast ◽  
Back Propagation Algorithm ◽  
Nonlinear Method ◽  
Flood Prediction ◽  
Artificial Neural

The establishing of a precise simulation model for runoff prediction in river with several tributaries is the difficulty of flood forecast, which is also one of the difficulties in hydrologic research. Due to the theory of Artificial Neural Network, using Back Propagation algorithm, the flood forecast model for ShiLiAn hydrologic station in Minjiang River is constructed and validated in this study. Through test, the result shows that the forecast accuracy is satisfied for all check standards of flood forecast and then proves the feasibility of using nonlinear method for flood forecast. This study provides a new method and reference for flood control and water resources management in the local region.

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