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Machine Learning Model Outperforms Linear Regression Models for 1- and 3-Year Post-Transplant Survival: Is This the Future?

R. W. Pettit1, S. J. Corr2, J. Havelka3, A. Rana1

1Baylor College of Medicine, Houston, TX, 2Interdisciplinary Surgical Technology and Innovation Center, Baylor College of Medicine, Houston, TX, 3InformAI, Houston, TX

Meeting: 2020 American Transplant Congress

Abstract number: B-312

Keywords: Area-under-curve (AUC)

Session Information

Session Name: Poster Session B: Biomarkers, Immune Assessment and Clinical Outcomes

Session Type: Poster Session

Date: Saturday, May 30, 2020

Session Time: 3:15pm-4:00pm

 Presentation Time: 3:30pm-4:00pm

Location: Virtual

*Purpose: Modeling is essential for regulatory oversight and risk stratification in clinical practice. Our hypothesis was that machine learning models would be superior to linear/logistic regression models in predicting 1- and 3-year mortality after liver transplantation.

*Methods: We tested this hypothesis by creating two machine learning predictive models, a Random Forest (RF) decision tree model and an AdaBoost ensemble-based model. We then compared their predictive capabilities to the commonly used linear/logistic regression (LR) method. We randomly selected 30,000 adult patients from the United Network for Organ Sharing database, including only those who underwent one orthotopic liver transplantation. To perform cross-validation, we randomly divided these 30,000 patients into training (60%) and test (40%) sets 5 times. All pre-transplantation parameters which would be known to a clinician at the time of transplant offer were included, totaling 342 features. We repeatedly trained a RF, an AdaBoost and a control LR model on the training data and tested the predictive power of these models on their respective test sets. We measured the average 5-fold cross-validated performance of these three models with classification accuracy, precision, recall, and area under the receiver operator curve (AUC) metrics.

*Results: We found that for both 1- and 3-year mortality prediction, the RF and AdaBoost models proved superior to LR methods. For the prediction of 1-year post-transplantation survival, the models performed against the test datasets as follows regarding average classification accuracy (CA) and AUC: (1) the AdaBoost model with CA = 0.848 and an AUC = 0.830, (2) the RF model with CA = 0.72 and AUC = 0.763, and (3) the LR model at CA = 0.667 and AUC = 0.582 respectively. Similarly, in modeling 3-year mortality, the models performed as follows: AdaBoost (0.832 CA and 0.818 AUC), RF model (CA = 0.705 and AUC = 0.721) and the LR model (CA = 0.666 and AUC = 0.579), respectively.

*Conclusions: The machine learning methods of RF and AdaBoost produced superior models for post-transplant survival compared to logistic regression, with the strongest performance form the AdaBoost model. This is likely due to the ability of these modeling techniques to find non-linear associations in the datasets. Based on these results, machine learning methods should be considered for incorporation into future transplant outcome models.

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To cite this abstract in AMA style:

Pettit RW, Corr SJ, Havelka J, Rana A. Machine Learning Model Outperforms Linear Regression Models for 1- and 3-Year Post-Transplant Survival: Is This the Future? [abstract]. Am J Transplant. 2020; 20 (suppl 3). https://atcmeetingabstracts.com/abstract/machine-learning-model-outperforms-linear-regression-models-for-1-and-3-year-post-transplant-survival-is-this-the-future/. Accessed May 11, 2025.

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