Predicting the survival of graft following liver transplantation using a nonlinear model.
Aim: The main purpose of this study is to introduce a high-accuracy model for predicting the best outcome of patients following liver transplantation. Subject and methods: Computer-based medical prognosis is becoming increasingly significant as the volume of medical records increases every day, maki...
| Publicado en: | Journal of Public Health: From Theory to Practice (2198-1833) Vol. 24; no. 5; pp. 443 - 453 |
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| Autores principales: | , |
| Formato: | Artículo |
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Springer Nature
Oct2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=118120223&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 118120223 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 21981833 NENI jtl: Journal of Public Health: From Theory to Practice (2198-1833) issn: 21981833 maglogo: N pubinfo: dt: Oct2016 vid: 24 iid: 5 pid: 237 pub: Springer Nature artinfo: ui: 118120223 10.1007/s10389-016-0742-7 ppf: 443 ppct: 10 formats: tig: atl: Predicting the survival of graft following liver transplantation using a nonlinear model. aug: au: Raji, C. Chandra, S. affil: Department of Computer Science and Engineering , Manonmaniam Sundaranar University , Tirunelveli India Computer Centre , University of Kerala , Thiruvananthapuram India keyword: Liver transplantation MELD Nonlinear model Survival prediction Liver transplantation MELD Nonlinear model Survival prediction ab: Aim: The main purpose of this study is to introduce a high-accuracy model for predicting the best outcome of patients following liver transplantation. Subject and methods: Computer-based medical prognosis is becoming increasingly significant as the volume of medical records increases every day, making manual processing harder. In addition, the inability of people to understand patterns from these huge volumes of data demands the use of machine learning tools. We propose an artificial neural network model to address the problem of organ allocation as well as survival prediction. This model extracts the relevant features and classifies the data set into training and test sets. Appropriate donor-recipient pairs were selected using ten-fold cross validation when training the medical data. Results: An accuracy of 99.74 % was represented by a multilayer perceptron artificial neural network model. We could observe that the graft survival rate with our data set using the MELD score was 79.17 %. We also tested our model with three existing works containing different data sets and proved that the highest accuracy was obtained in the model with our data set. Conclusion: To ensure accuracy, we made a comparison with existing models using various performance features. For training the model, we used a rich data set from the United Network for Organ Sharing transplant registry. We also carried out a survival analysis over 12 years, predicting survival probabilities using this rich data set and comparing it with existing approaches. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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