Adversarial Learning on Incomplete and Imbalanced Medical Data for Robust Survival Prediction of Liver Transplant Patients

The scarcity of liver transplants necessitates prioritizing patients based on their health condition to minimize deaths on the waiting list. Recently, machine learning methods have gained popularity for automatizing liver transplant allocation systems, which enables prompt and suitable selection of...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.73641-73650
Main Authors: Hallaji, Ehsan, Razavi-Far, Roozbeh, Palade, Vasile, Saif, Mehrdad
Format: Article
Language:English
Subjects:
class imbalance
Decision making
Deep learning
generative adversarial networks
Liver
liver transplantation
Liver transplants
Machine learning
Missing data
missing data imputation
Organ transplantation
Reliability
Resource management
Robustness
Survival
Survival prediction
Training
Transplants
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Summary:The scarcity of liver transplants necessitates prioritizing patients based on their health condition to minimize deaths on the waiting list. Recently, machine learning methods have gained popularity for automatizing liver transplant allocation systems, which enables prompt and suitable selection of recipients. Nevertheless, raw medical data often contain complexities such as missing values and class imbalance that reduce the reliability of the constructed model. This paper aims at eliminating the respective challenges to ensure the reliability of the decision-making process. To this aim, we first propose a novel deep learning method to simultaneously eliminate these challenges and predict the patients' survival chance. Secondly, a hybrid framework is designed that contains three main modules for missing data imputation, class imbalance learning, and classification, each of which employing multiple advanced techniques for the given task. Furthermore, these two approaches are compared and evaluated using a real clinical case study. The experimental results indicate the robust and superior performance of the proposed deep learning method in terms of F-measure and area under the receiver operating characteristic curve (AUC).
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3081040