Predicting the survival of graft following liver transplantation using a nonlinear model

Raji, C. G.; Chandra, Satish

doi:10.1007/s10389-016-0742-7

Cited by 15 publications

(6 citation statements)

References 17 publications

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“…True Negative (TN) and False Negative (FN) [9,10,11,12]. By using these parameters we calculated the performance measures like sensitivity, speci city and accuracy of the three proposed models [9,10,11,12].…”

Section: A Analysis Of Resultsmentioning

confidence: 99%

“…From the multi-organ data, we have selected the pancreas data for the proposed research which is in Table 1 2 shows that the multilayer perceptron model consists of an information layer which gets the input, output layer produces the predicted information and the yielded layer which approximating the values from the information layer. As the number of shrouded layers increase, the most accurate predicted output will be obtained from the output layer [9,10,11]. Multilayer perceptron regularly associated to govern the learning problems by training the input information, match the information and display the connection between the sources of information and yielded layers.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…Multilayer perceptron regularly associated to govern the learning problems by training the input information, match the information and display the connection between the sources of information and yielded layers. By using the shrouded layers, the multilayer perceptron adjusted the weight of the attributes so that the error in the data could be minimized [9,10,11]. Multilayer Perceptron with the help of back propagation train the data coming from the information layer reduces the error including root mean squared error with the help of shrouded layers and predicted the most accurate outcome from the yielded layers.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…Multilayer perceptron: Resulting work with multilayer perceptron has demonstrated that they are t for approximat-ing a XOR administrator just as numerous other non-straight capacities. A multilayer perceptron (MLP) is a supervised arti cial neural network model consisting of more than one perceptron[9,10,11]. The Figure…”

mentioning

confidence: 99%

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Artificial Neural Networks Model in the Prediction of Pancreas Transplantation Recipient Survival

Raji¹,

S.N²

2022

Preprint

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The pancreas transplantation is always a risky procedure, so that the number of transplantation suggesting by doctors are less. Also doctors have no method to predict the survival of patient after pancreas transplantation. So it is necessary to have a system to predict the survival of patients after pancreas transplantation. Mostly Type 1 diabetes are treated through pancreas transplantation. Yet, it is commonly held for those with genuine diabetes difficulties, on the grounds that the reactions of a pancreas transplant are huge. Consequently the requirement for this investigation is basic. Pancreas transplantation is constantly dangerous in light of it’s eventual outcomes, so the proposed framework anticipating the odds of survival of patients by breaking down their wellbeing conditions. This investigation will be useful to specialists for understanding careful results and expands the believability of specialists. From our research, we can see that the chosen model have given greater survival rate. Pancreas transplantation is definitely not a standard procedure yet a seldom happening procedure, since it is an exceptionally unsafe technique and has numerous eventual outcomes. Need an exact model to foresee the survival after transplantation. We proposed three models which are suitable for pancreas survival prediction. From that, with Multilayer Perceptron we obtained 99.5% accuracy in survival prediction. The result was compared with existing research work to ensure that it is the only accurate method now existing to predict the patients survival in pancreas transplantation.

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Section: A Analysis Of Resultsmentioning

confidence: 99%

Section: Proposed Methodologymentioning

confidence: 99%

Section: Proposed Methodologymentioning

confidence: 99%

mentioning

confidence: 99%

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Artificial Neural Networks Model in the Prediction of Pancreas Transplantation Recipient Survival

Raji¹,

S.N²

2022

Preprint

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“…By considering the square root of the relative squared error, we can reduce the fallacy of the identical measurements as the predicted consignment [18]. We can determine these measures by considering the AUC [19].…”

Section: Performance Error Measuresmentioning

confidence: 99%

Computational Methods for Predicting the Outcome of Thoracic Transplantation

Raji¹,

A.K²

2022

Preprint

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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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