Prediction of Kidney Graft Rejection Using Artificial Neural Network / 대한의료정보학회지
Healthcare Informatics Research
; : 277-284, 2017.
Article
em En
| WPRIM
| ID: wpr-195861
Biblioteca responsável:
WPRO
ABSTRACT
OBJECTIVES: Kidney transplantation is the best renal replacement therapy for patients with end-stage renal disease. Several studies have attempted to identify predisposing factors of graft rejection; however, the results have been inconsistent. We aimed to identify prognostic factors associated with kidney transplant rejection using the artificial neural network (ANN) approach and to compare the results with those obtained by logistic regression (LR). METHODS: The study used information regarding 378 patients who had undergone kidney transplantation from a retrospective study conducted in Hamadan, Western Iran, from 1994 to 2011. ANN was used to identify potential important risk factors for chronic nonreversible graft rejection. RESULTS: Recipients' age, creatinine level, cold ischemic time, and hemoglobin level at discharge were identified as the most important prognostic factors by ANN. The ANN model showed higher total accuracy (0.75 vs. 0.55 for LR), and the area under the ROC curve (0.88 vs. 0.75 for LR) was better than that obtained with LR. CONCLUSIONS: The results of this study indicate that the ANN model outperformed LR in the prediction of kidney transplantation failure. Therefore, this approach is a promising classifier for predicting graft failure to improve patients' survival and quality of life, and it should be further investigated for the prediction of other clinical outcomes.
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Texto completo:
1
Índice:
WPRIM
Assunto principal:
Qualidade de Vida
/
Modelos Logísticos
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Causalidade
/
Estudos Retrospectivos
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Fatores de Risco
/
Curva ROC
/
Transplante de Rim
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Terapia de Substituição Renal
/
Transplantes
/
Creatinina
Tipo de estudo:
Etiology_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
País/Região como assunto:
Asia
Idioma:
En
Revista:
Healthcare Informatics Research
Ano de publicação:
2017
Tipo de documento:
Article