Intuitionistic fuzzy RFE-based prognostic model for liver transplantation
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Abstract
Background: Survival prediction after liver transplantation is a very challenging but complex task. LT is often the best treatment for advanced liver disease, provided no other medical conditions contraindicate it. This article explores clinical predictors, such as MELD scores and hormone levels, along with computational algorithms for forecasting post-transplant survival.
Objective: This study evaluates the performance of machine learning models for predicting survival outcomes in liver transplant recipients using UNOS data. It develops and validates a donor and recipient-based prognostic model.
Materials and methods: The UNOS database contains 65,535 donor-recipient pairs in transplants conducted in the U.S. between October 1987 and June 2021, with 421 attributes. The top 24 features, including logistic regression, random forest, artificial neural networks, XGBoost, CART, and K-nearest neighbors, were used to train the models upon feature selection. Models were compared using AUROC, accuracy, specificity, sensitivity, and precision.
Results: ANN outperformed other models for the UNOS dataset, with an AUROC of 0.98–0.99. Validated results in the KCH dataset are robust at AUROC: 0.94–0.95.
Conclusion: The model offered exceptional generalizability performance to guide clinical decisions in transplantation support, yet variability in patients’ characteristics may differ significantly among the cohorts and impact the results.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Personal views expressed by the contributors in their articles are not necessarily those of the Journal of Associated Medical Sciences, Faculty of Associated Medical Sciences, Chiang Mai University.
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