Application of multilayer perceptron and radial basis function neural networks in differentiating between chronic obstructive pulmonary and congestive heart failure diseases

Mehrabi, Saeed; Maghsoudloo, Mehran; Arabalibeik, Hossein; Noormand, Rezvan; Nozari, Younes

doi:10.1016/j.eswa.2008.08.039

Cited by 37 publications

(19 citation statements)

References 25 publications

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“…Time-domain indices were proved to be more capable of discriminating the normal from CHF signals than Poincare plot features (Khaled et al, 2006). Mehrabi, Maghsoudloo, Arabalibeik, Noormand, and Nozari (2009) differentiated between patients suffering from CHF and chronic obstructive pulmonary disease. They employed multilayer perceptron and radial basis function neural networks and finally obtained fine classification (Mehrabi et al, 2009).…”

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confidence: 99%

Classification of congestive heart failure with different New York Heart Association functional classes based on heart rate variability indices and machine learning

Liu

2019

Expert Systems

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This study aims to evaluate the effect of heart rate variability (HRV) indices on the New York Heart Association (NYHA) classification of patients with congestive heart failure and to test the effectiveness of different machine learning algorithms. Twenty‐nine long‐term RR interval recordings from subjects (aged 34 to 79) with congestive heart failure (NYHA classes I, II, and III) in MIT‐BIH Database were studied. We firstly removed the unreasonable RR intervals and segment the RR recordings with a 300‐RR interval length window. Then the multiple HRV indexes were calculated for each RR segment. Support vector machine (SVM) and classification and regression tree (CART) methods were then separately used to distinguish patients with different NYHA classes based on the selected HRV indices. Receiver operating characteristic curve analysis was finally employed as the evaluation indicator to compare the performance of the two classifiers. The SVM classifier achieved accuracy, sensitivity, and specificity of 84.0%, 71.2%, and 83.4%, respectively, whereas the CART classifier achieved 81.4%, 66.5%, and 81.6%, respectively. The area under the curve of receiver operating characteristic for the two classifiers was 86.4% and 84.7%, respectively. It is possible for accurately classifying the NYHA functional classes I, II, and III when using the combination of HRV indices and machine learning algorithms. The SVM classifier performed better in classification than the CART classifier using the same HRV indices.

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confidence: 99%

Classification of congestive heart failure with different New York Heart Association functional classes based on heart rate variability indices and machine learning

Liu

2019

Expert Systems

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“…That is, a three-layer MLP needs at least d þ 1 hidden neurons to achieve second-order approximation and at least one hidden unit to achieve linear approximation. So, considering the foresaid suggestions, we take two initial hidden nodes in our comparisons [1,36]. Another difficult problem is how to determine the optimal number of hidden nodes.…”

Section: Methodsmentioning

confidence: 99%

Plane-Gaussian artificial neural network

Yang

Chen

2011

Neural Comput & Applic

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Multilayer perceptrons (MLPs) and radial basis functions networks (RBFNs) have been widely concerned in recent years. In this paper, based on k-plane clustering (kPC) algorithm, we propose a novel artificial network model termed as Plane-Gaussian network to enlarge the arsenal of the neural networks. This network adopts a so-called Plane-Gaussian activation function (PGF) in hidden neurons. Replacing traditional central point of Gaussian radial basis function (RBF) with central hyperplane, PGF forms a band-shaped rather than spheral-shaped receptive field in RBF, which makes PGF able to express its peculiar geometrical characteristics: locality and globality. Importantly, it is also proved that PGF network (PGFN) having one hidden layer is capable of universal approximation. As a universal approximator, PGFN gives an informal way of bridging the gap between MLP and RBFN. The experiments report comparison between training time and classification accuracies on some artificial and UCI datasets and conclude that (1) PGFN runs significantly faster than MLP and (2) PGFN has comparable or better classification performance than MLP and RBFN, especially in subspace-distributed datasets.

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“…Otherwise, it is classified as non-donor. Following Mehrabi et al [80], the organ donation dataset was partitioned into a training sample, which comprises the data records used to train the neural network, a testing sample, which is an independent set of data records used to track errors during training in order to prevent over-fitting, and a holdout sample, which is another independent set of data records used to assess the final neural network. The error for the holdout sample gives an ''honest'' estimate of the predictive ability of the model because the holdout cases were not used to build the model.…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

Market segmentation of organ donors in Egypt: a bio-inspired computational intelligence approach

Mostafa

Toksarı

2011

Neural Comput & Applic

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First performed in 1954, organ transplantation is a universally practiced clinical procedure. This study uses ant colony optimization (ACO), radial basis function neural network (RBFNN), Kohonen's self-organizing maps (SOM), and support vector machines (SVMs) to examine the effect of various cognitive, psychographic, and attitudinal factors on organ donation. ACO, RBFNN, SOM, and SVMs are compared to a standard statistical method (linear discriminant analysis [LDA]). The variable sets considered are altruistic values, perceived risks/benefits, knowledge, attitudes toward organ donation, and intention to donate organs. The paper shows how it is possible to identify various dimensions of organ donation behavior by uncovering complex patterns in the dataset and also shows the classification and clustering abilities of machine-learning systems.

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Application of multilayer perceptron and radial basis function neural networks in differentiating between chronic obstructive pulmonary and congestive heart failure diseases

Cited by 37 publications

References 25 publications

Classification of congestive heart failure with different New York Heart Association functional classes based on heart rate variability indices and machine learning

Classification of congestive heart failure with different New York Heart Association functional classes based on heart rate variability indices and machine learning

Plane-Gaussian artificial neural network

Market segmentation of organ donors in Egypt: a bio-inspired computational intelligence approach

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