Statistical methods versus machine learning techniques for donor-recipient matching in liver transplantation

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Research areas:
Year:
2021
Type of Publication:
Article
Keywords:
machine learning, statistical techniques, donor-recipient matching, liver transplant, transplantation, liver, liver transplantation, UNOS database, UNOS
Authors:
Journal:
PLoS One
Volume:
16
Number:
5
Pages:
e0252068
Month:
Mayo
Note:
JCR(2021): 3.752 Position: 29/73 (Q2) Category: MULTIDISCIPLINARY SCIENCES
Abstract:
Donor-Recipient (D-R) matching is one of the main challenges to be fulfilled nowadays. Due to the increasing number of recipients and the small amount of donors in liver transplantation, the allocation method is crucial. In this paper, to establish a fair comparison, the United Network for Organ Sharing database was used with 4 different end-points (3 months, and 1, 2 and 5 years), with a total of 39, 189 D-R pairs and 28 donor and recipient variables. Modelling techniques were divided into two groups: 1) classical statistical methods, including Logistic Regression (LR) and Naïve Bayes (NB), and 2) standard machine learning techniques, including Multilayer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB) or Support Vector Machines (SVM), among others. The methods were compared with standard scores, MELD, SOFT and BAR. For the 5-years end-point, LR (AUC = 0.654) outperformed several machine learning techniques, such as MLP (AUC = 0.599), GB (AUC = 0.600), SVM (AUC = 0.624) or RF (AUC = 0.644), among others. Moreover, LR also outperformed standard scores. The same pattern was reproduced for the others 3 end-points. Complex machine learning methods were not able to improve the performance of liver allocation, probably due to the implicit limitations associated to the collection process of the database.
Comments:
JCR(2021): 3.752 Position: 29/73 (Q2) Category: MULTIDISCIPLINARY SCIENCES
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