Long-Term Forecasting the Survival in Liver Transplantation Using Multilayer Perceptron Networks

Raji, C. G.; Chandra, Satish

doi:10.1109/tsmc.2017.2661996

Cited by 31 publications

(18 citation statements)

References 19 publications

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“…The NN‐based classifier performance depends on the model selected and the appropriate training of the model. We have trained an MLP [45] for the multiclass classification with some non‐linearity, described for input feature vector

bold-italicx

O^{0} = x, O^{l} = F^{l} ()(, W^{l} {\overset{false^}{o}}^{()(, l - 1)}) for l = 1, \dots, L .

The input vector

bold-italicx

is taken as the ‘output of the zeroth layer’. A hat notation

{\hat{o}}^{()(, l - 1)}

represents an operation where a number

1

is prepended to a vector to increase its dimension.…”

Section: Proposed Methodsmentioning

confidence: 99%

Classification of magnetic resonance images for brain tumour detection

Kurmi

Chaurasia

2020

IET image process

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bold-italicx

O^{0} = x, O^{l} = F^{l} ()(, W^{l} {\overset{false^}{o}}^{()(, l - 1)}) for l = 1, \dots, L .

The input vector

bold-italicx

is taken as the ‘output of the zeroth layer’. A hat notation

{\hat{o}}^{()(, l - 1)}

represents an operation where a number

1

is prepended to a vector to increase its dimension.…”

Section: Proposed Methodsmentioning

confidence: 99%

Classification of magnetic resonance images for brain tumour detection

Kurmi

Chaurasia

2020

IET image process

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“…MLP model is a class of feed forward ANNs [12]. The term MLP is employed inconclusively or else loosely to ask any feed forward ANN's otherwise it is strictly used to define networks constituted of multiple layers of perceptron.…”

Section: Multilayer Perceptron Annmentioning

confidence: 99%

“…MLP is a perceptron however there is the added complexity through the advent of layers [12]. We can see three types of layers in an MLP which consist of an input, hidden and output layers [12]. MLP consists of more than one collinear layer of neurons.…”

Section: Multilayer Perceptron Annmentioning

confidence: 99%

“…While considering a simple three layer perceptron, the primary layer is input layer and final is the output layer and middle layer is called as hidden layer. We grub our input file into the primary input layer and the product is taken from the output layer [12]. We will be able to raise the amount of the hidden layer as much as we wish, to build the model more composite according to our task.…”

Section: Multilayer Perceptron Annmentioning

confidence: 99%

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Computational Methods for Predicting the Outcome of Thoracic Transplantation

Raji¹,

A.K²

2022

Preprint

Self Cite

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Cardiac disease and the death rates due to coronary heart failure and cardiomyopathy are increasing. Thoracic transplantation is now a widely accepted therapeutic option for end-stage cardiac failure. The survival rate after the organ transplantation is crucial. Survival prediction after heart transplantation is a hot area of research. The use of conventional statistical techniques is computationally expensive and does not provide reliable solutions. Artificial Neural Networks based survival prediction helps surgeons make precise decisions and predict the best outcomes. The proposed system implements multi-layer perceptron algorithm, which shows good performance in survival prediction. We also implemented our work in the Radial Basis Function Network model to prove the accuracy of proposed model. For this research study, data were collected from United Network for Organ Sharing database and extracted the relevant thoracic transplantation survival prediction attributes with the help of suitable data mining techniques. We obtained an accuracy of 97.1% from the multi-layer perceptron model with the evaluation of various performance measures. In order to assure the validity of the proposed model we implemented the Radial Basis Function model and obtained an accuracy of 92.37%. We collated the accuracy of proposed survival prediction models with existing systems and proved that the proposed system appeared to be best for survival prediction with higher accuracy compared to 85.9% in the existing system. The outcome of the model will be an asset for the lifesaving procedures in the medical field.

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“…These models include Ridgelet ANN [7], bagging neural network [8], and Multilayer Perceptron Networks [9].…”

Section: Introductionmentioning

confidence: 99%

Multistep Wind Speed and Wind Power Prediction Based on a Predictive Deep Belief Network and an Optimized Random Forest

Sun

Zhang

2018

Mathematical Problems in Engineering

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A variety of supervised learning methods using numerical weather prediction (NWP) data have been exploited for short-term wind power forecasting (WPF). However, the NWP data may not be available enough due to its uncertainties on initial atmospheric conditions. Thus, this study proposes a novel hybrid intelligent method to improve existing forecasting models such as random forest (RF) and artificial neural networks, for higher accuracy. First, the proposed method develops the predictive deep belief network (DBN) to perform short-term wind speed prediction (WSP). Then, the WSP data are transformed into supplementary input features in the prediction process of WPF. Second, owing to its ensemble learning and parallelization, the random forest is used as supervised forecasting model. In addition, a data driven dimension reduction procedure and a weighted voting method are utilized to optimize the random forest algorithm in the training process and the prediction process, respectively. The increasing number of training samples would cause the overfitting problem. Therefore, the k-fold cross validation (CV) technique is adopted to address this issue. Numerical experiments are performed at 15-min, 30-min, 45-min, and 24-h to indicate the superiority and signal advantages compared with existing methods in terms of forecasting accuracy and scalability.

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Long-Term Forecasting the Survival in Liver Transplantation Using Multilayer Perceptron Networks

Cited by 31 publications

References 19 publications

Classification of magnetic resonance images for brain tumour detection

Classification of magnetic resonance images for brain tumour detection

Computational Methods for Predicting the Outcome of Thoracic Transplantation

Multistep Wind Speed and Wind Power Prediction Based on a Predictive Deep Belief Network and an Optimized Random Forest

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