scholarly journals ARTIFICIAL NEURAL NETWORKS AND LOGISTIC REGRESSION WITH AN ORDERED CATEGORICAL RESPONSE: STUDY CHRONIC KIDNEY DISEASE

2016 ◽  
Vol 16 (1) ◽  
pp. 67-91
Keyword(s):  
Neural Networks ◽  
Chronic Kidney Disease ◽  
Logistic Regression ◽  
Artificial Neural Networks ◽  
Kidney Disease ◽  
Artificial Neural ◽  
Categorical Response ◽  
Ordered Categorical

An end stage kidney disease predictor based on an artificial neural networks ensemble

2013 ◽  
Vol 40 (11) ◽  
pp. 4438-4445 ◽  
Author(s):  
Tommaso Di Noia ◽  
Vito Claudio Ostuni ◽  
Francesco Pesce ◽  
Giulio Binetti ◽  
David Naso ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Kidney Disease ◽  
End Stage Kidney Disease ◽  
Artificial Neural ◽  
Neural Networks Ensemble ◽  
End Stage

Comparison of Artificial Neural Networks with Logistic Regression for Detection of Obesity

2011 ◽  
Vol 36 (4) ◽  
pp. 2449-2454 ◽  
Author(s):  
Seyed Taghi Heydari ◽  
Seyed Mohammad Taghi Ayatollahi ◽  
Najaf Zare
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Artificial Neural Networks ◽  
Artificial Neural

Methods of Evaluation for Region’s Landslide Susceptibility. Short Overview

2017 ◽  
Vol 6 (3) ◽  
pp. 57-60
Author(s):  
Денис Кривогуз ◽  
Denis Krivoguz
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Landslide Susceptibility ◽  
Susceptibility Assessment ◽  
Linear Discriminant ◽  
Landslide Susceptibility Assessment ◽  
Artificial Neural

Modern approaches to the region’s landslide susceptibility assessment are considered in this paper. Have been presented descriptions of the most used techniques for landslide susceptibility assessment: logistic regression, indicator validity, linear discriminant analysis and application of artificial neural networks. These techniques’ advantages and disadvantages are discussed in the paper. The most suitable techniques for various conditions of analysis have been marked. It has been concluded that the most acceptable techniques of analysis for a large number of input data related to the studied region are the method of logistic regression and indicator validity method. With these methods the most accurate results are achieved. When there is a lack of information, it is more expedient to use linear discriminant analysis and artificial neural networks that will minimize potential analysis inaccuracies.

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