The Role of Discretization of Continuous Variables in Socioeconomic Classification Models on the Example of Logistic Regression Models and Artificial Neural Networks

2020 ◽  
pp. 35-51
Author(s):  
Wioletta Grzenda
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Artificial Neural Networks ◽  
Regression Models ◽  
Continuous Variables ◽  
Classification Models ◽  
Logistic Regression Models ◽  
Artificial Neural

scholarly journals A Comparison of Artificial Neural Network and Decision Trees with Logistic Regression as Classification Models for Breast Cancer Survival

2020 ◽  
Vol 5 (6) ◽  
pp. 1170-1190
Author(s):  
Venkateswara Rao Mudunuru ◽  
Leslaw A. Skrzypek
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Logistic Regression ◽  
Artificial Neural Networks ◽  
Decision Trees ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Classification Models ◽  
Classification Methods ◽  
Artificial Neural

In the field of medicine, several recent studies have shown the value of Artificial Neural Networks, decision trees, logistic regression are playing a major role as the predictor, and classification methods. The research has been expanded to estimate the incidence of breast, lung, liver, ovarian, cervical, bladder and skin cancer. The main aim of this paper is to develop models of logistic regression, Artificial Neural Networks, and Decision trees using the same input and output variables and to compare their success in predicting breast cancer survival in woman. To find the best model for breast cancer survival, the sensitivity and specificity of all these models are measured and evaluated with their respective confidence intervals and the ROC values.

Technique of scientific and technical forecasting using neural networks

2018 ◽  
pp. 103-110
Author(s):  
V. V. Nefedev
Keyword(s):  
Mathematical Modeling ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Original Method ◽  
Forecasting Methods ◽  
Expert Assessments ◽  
Artificial Neural

For the definition and implementation of breakthrough technologies the most important is the role of scientific and technical forecasting. Well-known forecasting methods based on extrapolation, expert assessments and mathematical modeling are not universal and have a number of significant disadvantages. The article proposes an original method of scientific and technical forecasting based on the use of the methodology of artificial neural networks. 

scholarly journals ARTIFICIAL NEURAL NETWORKS FOR MODELING HYPSOMETRIC RELATIONSHIPS OF Pinus caribaea Morelet var. caribaea Barr. & Golf.

2018 ◽  
Vol 36 (4) ◽  
pp. 891
Author(s):  
Ouorou Ganni Mariel GUERA ◽  
José Antônio Aleixo SILVA ◽  
Rinaldo Luiz Caraciolo FERREIRA ◽  
Héctor Barrero MEDEL ◽  
Daniel Álvarez LAZO
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sample Size ◽  
Regression Models ◽  
Growth Models ◽  
Pinus Caribaea ◽  
Three Stages ◽  
Artificial Neural ◽  
Sigmoidal Growth ◽  
Pinus Caribaea Morelet

The present study was carried out to compare the performances of regression models and Artificial Neural  Networks (ANNs) in hypsometric relationships modeling and to analyze the influence of ANN type  and sample size on ANN performance. The database was consisted by 65 circular plots of 500 m² in which  Diameter at Breast Height - DBH (cm) and Total Height - Ht (m) of 2538 trees were measured in plantations of Pinus caribaea var. caribaea in Macurije forest company, Cuba. The study was carried out in three  stages: i) Fit of traditional hypsometric models and sigmoidal growth models; ii) ANNs training and comparison of the selected ANN with the regression model selected; iii) Analysis of sample size and ANN type influences on the estimates precision by means of a completely random experimental design with 5x2 factorial arrangement, with the factors sample size (N) and ANN type (R). The results indicated that the best equation to estimate trees heights was that of Gompertz. The ANNs MLP 1-4-1 and MLP 8-4-1 were superior to the selected equation (Gompertz). Multi-Layer Perceptron ANNs generated more accurate estimates and their performances were less influenced by the sample size.

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