Artificial nonmonotonic neural networks

Boutsinas, Basilis; Vrahatis, Michael N.

doi:10.1016/s0004-3702(01)00126-6

Cited by 31 publications

(15 citation statements)

References 52 publications

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“…Neural networks lack centralized control in the classical sense, since all the interconnected processing elements change or ''adapt'' simultaneously with the flow of information and adaptive rules [41][42][43]. Several reviews on ANN have been published recently [41,[43][44][45][46][47][48][49][50]. Although there are several types of neural networks, multilayer perceptron is the most commonly used network.…”

Section: What Is An Artificial Neural Network?mentioning

confidence: 99%

Models for prediction of mortality from cirrhosis with special reference to artificial neural network: a critical review

Ghoshal

Das

2007

Hepatol Int

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Prediction of mortality of patients with cirrhosis of liver, a common and potentially fatal disease, is important for timely listing of patients for liver transplantation. The Child-Pugh scoring system has been widely used for predicting the outcome of liver cirrhosis. The Model for End-Stage Liver Disease (MELD) score has recently become popular for prediction of short-term mortality for organ allocation. A few studies that evaluated artificial neural network (ANN)-based model for prediction of outcome of cirrhosis of liver in terms of mortality have consistently shown it to be superior to Child-Pugh scoring and logistic regression-based models; it is worth noting that MELD score is also derived using the logistic regression model. Due to the inherent ability of neural network-based systems in identifying complex nonlinear interactions, ANN-based models are expected to perform better than most linear models, such as regression-based models. More studies are needed on ANN-based models for prediction of mortality of patients with cirrhosis of liver and its value in prioritization of organ allocation for treatment of patients with cirrhosis of liver.

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Section: What Is An Artificial Neural Network?mentioning

confidence: 99%

Models for prediction of mortality from cirrhosis with special reference to artificial neural network: a critical review

Ghoshal

Das

2007

Hepatol Int

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“…Artificial neural network analysis Several reviews on ANN have been published recently [8][9][10][11][12][13][14][15][16]. In order to design the ANN, we used the Neural Network Toolbox of the MATLAB 7 software.…”

Section: Effective Variablesmentioning

confidence: 99%

Artificial Intelligence Models for Predicting Iron Deficiency Anemia and Iron Serum Level Based on Accessible Laboratory Data

2011

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Iron deficiency anemia (IDA) is the most common nutritional deficiency worldwide. Measuring serum iron is time consuming, expensive and not available in most hospitals. In this study, based on four accessible laboratory data (MCV, MCH, MCHC, Hb/RBC), we developed an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) to diagnose the IDA and to predict serum iron level. Our results represent that the neural network analysis is superior to ANFIS and logistic regression models in diagnosing IDA. Moreover, the results show that the ANN is likely to provide an accurate test for predicting serum iron levels with high accuracy and acceptable precision.

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“…In a future correspondence we intend to apply various other networks and learning techniques including non-monotone neural networks [11], probabilistic neural networks [122], selforganized maps [58], recurrent networks and radial basis function networks [41] among others. All data sets used in our experiments are available upon request.…”

Section: Topologymentioning

confidence: 99%