Artificial intelligence in liver transplantation: Opportunities and challenges

Table 3 Commonly used machine learning model in liver transplant

Random forest	Can predict post-transplant outcomes
Decision tree	Useful for classification and regression tasks
Logistic regression	Useful in predicting binary outcomes like survival or graft failure
Support vector machines	Useful in classification, regression, and outlier detection
	can predict patient outcomes from medical records and diagnose diseases
K-Nearest neighbors	Classifies data points based on their proximity to other data points assuming they share similar traits
	Can predict patient outcomes by comparing new data to historical cases
Artificial neural networks	Based on human brain. Consists of multiple layers of neurons
	Useful in image, speech recognition and predictive models
Gradient boosting machines	Ensemble learning method
	Useful in regression and classification problems
	Can handle large scale data
XGBoost	Advanced form of GBM
	Less overfitting as compared to GBM
AdaBoost	Ensemble learning method
	Useful in image classification, sentiment analysis, and fraud detection
RuleFit	Ensemble learning method
	combines decision trees and linear models to form interpretable rules that reveal data patterns
	useful for understanding decisions, especially in regulatory contexts
Tab transformers	Useful to handle tabular data

GBM: Gradient boosting machine; XGBoost: eXtreme Gradient Boosting.