scholarly journals Review of Input Variable Selection Methods for Artificial Neural Networks

10.5772/16004 ◽  
2011 ◽  
Author(s):  
Robert May ◽  
Graeme Dandy ◽  
Holger Maier
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Variable Selection ◽  
Selection Methods ◽  
Artificial Neural

scholarly journals Artificial neural networks versus LASSO regression for the prediction of long-term survival after surgery for invasive IPMN of the pancreas

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0249206
Author(s):  
Linus Aronsson ◽  
Roland Andersson ◽  
Daniel Ansari
Keyword(s):  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Variable Selection ◽  
Lasso Regression ◽  
Term Survival ◽  
Long Term Survival ◽  
Artificial Neural ◽  
Invasive Ipmn

Prediction of long-term survival in patients with invasive intraductal papillary mucinous neoplasm (IPMN) of the pancreas may aid in patient assessment, risk stratification and personalization of treatment. This study aimed to investigate the predictive ability of artificial neural networks (ANN) and LASSO regression in terms of 5-year disease-specific survival. ANN work in a non-linear fashion, having a potential advantage in analysis of variables with complex correlations compared to regression models. LASSO is a type of regression analysis facilitating variable selection and regularization. A total of 440 patients undergoing surgical treatment for invasive IPMN of the pancreas registered in the Surveillance, Epidemiology and End Results (SEER) database between 2004 and 2016 were analyzed. The dataset was prior to analysis randomly split into a modelling and test set (7:3). The accuracy, precision and F1 score for predicting mortality were 0.82, 0.83 and 0.89, respectively for ANN with variable selection compared to 0.79, 0.85 and 0.87, respectively for the LASSO-model. ANN using all variables showed similar accuracy, precision and F1 score of 0.81, 0.85 and 0.88, respectively compared to a logistic regression analysis. McNemar´s test showed no statistical difference between the models. The models showed high and similar performance with regard to accuracy and precision for predicting 5-year survival status.

Classification of apple beverages using artificial neural networks with previous variable selection

2004 ◽  
Vol 524 (1-2) ◽  
pp. 225-234 ◽  
Author(s):  
M. Gestal ◽  
M.P. Gómez-Carracedo ◽  
J.M. Andrade ◽  
J. Dorado ◽  
E. Fernández ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Variable Selection ◽  
Artificial Neural

Non-linear variable selection for artificial neural networks using partial mutual information

2008 ◽  
Vol 23 (10-11) ◽  
pp. 1312-1326 ◽  
Author(s):  
Robert J. May ◽  
Holger R. Maier ◽  
Graeme C. Dandy ◽  
T.M.K. Gayani Fernando
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mutual Information ◽  
Variable Selection ◽  
Non Linear ◽  
Selection For ◽  
Artificial Neural
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