Graft rejection prediction following kidney transplantation using machine learning techniques: A systematic review and meta-analysis

Aldilas Achmad Nursetyo, Shabbir Syed-Abdul, Mohy Uddin, Yu Chuan Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Kidney transplantation is recommended for patients with End-Stage Renal Disease (ESRD). However, complications, such as graft rejection are hard to predict due to donor and recipient variability. This study discusses the role of machine learning (ML) in predicting graft rejection following kidney transplantation, by reviewing the available related literature. PubMed, DBLP, and Scopus databases were searched to identify studies that utilized ML methods, in predicting outcome following kidney transplants. Fourteen studies were included. This study reviewed the deployment of ML in 109,317 kidney transplant patients from 14 studies. We extracted five different ML algorithms from reviewed studies. Decision Tree (DT) algorithms revealed slightly higher performance with overall mean Area Under the Curve (AUC) for DT (79.5% + 0.06) was higher than Artificial Neural Network (ANN) (78.2% + 0.08). For predicting graft rejection, ANN and DT were at the top among ML models that had higher accuracy and AUC.

Original languageEnglish
Title of host publicationMEDINFO 2019
Subtitle of host publicationHealth and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics
EditorsBrigitte Seroussi, Lucila Ohno-Machado, Lucila Ohno-Machado, Brigitte Seroussi
PublisherIOS Press
Pages10-14
Number of pages5
ISBN (Electronic)9781643680026
DOIs
Publication statusPublished - Aug 21 2019
Event17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, France
Duration: Aug 25 2019Aug 30 2019

Publication series

NameStudies in Health Technology and Informatics
Volume264
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

Conference17th World Congress on Medical and Health Informatics, MEDINFO 2019
CountryFrance
CityLyon
Period8/25/198/30/19

    Fingerprint

Keywords

  • Graft Rejection
  • Kidney Transplantation
  • Machine Learning

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

Cite this

Nursetyo, A. A., Syed-Abdul, S., Uddin, M., & Li, Y. C. (2019). Graft rejection prediction following kidney transplantation using machine learning techniques: A systematic review and meta-analysis. In B. Seroussi, L. Ohno-Machado, L. Ohno-Machado, & B. Seroussi (Eds.), MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics (pp. 10-14). (Studies in Health Technology and Informatics; Vol. 264). IOS Press. https://doi.org/10.3233/SHTI190173