An ensemble approach for ordinal threshold models applied to liver transplantation

Pérez-Ortiz, María; Gutiérrez, Pedro Antonio; Hervás-Martı́nez, César; Briceño, Javier; Mata, Manuel de la

doi:10.1109/ijcnn.2012.6252755

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…This is the most traditional technique in the area of organ transplantation survival [6]. Jeffrey M. Venstrom, et al conducted a study consisting of individuals with diabetes and early or no kidney ailment might be possibility for a pancreas transplant alone (lone pan-creas transplant) [7]. A pancreas transplant medical procedure includes just a pancreas transplant without different medical procedures [7,8].…”

Section: Background Of Existing Solutionsmentioning

confidence: 99%

“…Jeffrey M. Venstrom, et al conducted a study consisting of individuals with diabetes and early or no kidney ailment might be possibility for a pancreas transplant alone (lone pan-creas transplant) [7]. A pancreas transplant medical procedure includes just a pancreas transplant without different medical procedures [7,8]. Robert R. Red eld, et al conducted a study consisting of Diabetes patients with perpetual kidney sickness (CKD) experience unreasonable grimness and mortality [8].…”

Section: Background Of Existing Solutionsmentioning

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: Background Of Existing Solutionsmentioning

confidence: 99%

Section: Background Of Existing Solutionsmentioning

confidence: 99%

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

Raji¹,

S.N²

2022

Preprint

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Ordinal Evolutionary Artificial Neural Networks for Solving an Imbalanced Liver Transplantation Problem

Dorado-Moreno

Pérez-Ortiz

Ayllón-Terán

et al. 2016

Lecture Notes in Computer Science

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Projection-Based Ensemble Learning for Ordinal Regression

Pérez-Ortiz

Gutiérrez

Hervás-Martı́nez

2014

IEEE Trans. Cybern.

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Abstract-The classification of patterns into naturally ordered labels is referred to as ordinal regression. This paper proposes an ensemble methodology specifically adapted to this type of problems, which is based on computing different classification tasks through the formulation of different order hypotheses. Every single model is trained in order to distinguish between one given class (k) and all the remaining ones, but grouping them in those classes with a rank lower than k, and those with a rank higher than k. Therefore, it can be considered as a reformulation of the well-known one-versus-all scheme. The base algorithm for the ensemble could be any threshold (or even probabilistic) method, such as the ones selected in this paper: kernel discriminant analysis, support vector machines and logistic regression (all reformulated to deal with ordinal regression problems). The method is seen to be competitive when compared with other state-of-the-art methodologies (both ordinal and nominal), by using six measures and a total of fifteen ordinal datasets. Furthermore, an additional set of experiments is used to study the potential scalability and interpretability of the proposed method when using logistic regression as base methodology for the ensemble.

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An ensemble approach for ordinal threshold models applied to liver transplantation

Cited by 3 publications

References 22 publications

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

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

Ordinal Evolutionary Artificial Neural Networks for Solving an Imbalanced Liver Transplantation Problem

Projection-Based Ensemble Learning for Ordinal Regression

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