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

Ehsan Hallaji, Roozbeh Razavi-Far, Vasile Palade, Mehrdad Saif

    Research output: Contribution to journalArticlepeer-review

    Abstract

    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).

    Original languageEnglish
    Article number9432931
    Pages (from-to)73641-73650
    Number of pages10
    JournalIEEE Access
    Volume9
    Early online date17 May 2021
    DOIs
    Publication statusPublished - 24 May 2021

    Keywords

    • class imbalance
    • decision-making
    • generative adversarial networks
    • liver transplantation
    • missing data imputation
    • Survival prediction

    ASJC Scopus subject areas

    • Computer Science(all)
    • Materials Science(all)
    • Engineering(all)

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