Copyright ©The Author(s) 2021.
World J Transplant. Jul 18, 2021; 11(7): 277-289
Published online Jul 18, 2021. doi: 10.5500/wjt.v11.i7.277
Table 1 Different machine learning categories

Supervised learning
Unsupervised learning
Reinforcement learning
DatasetLabeled (input and output are known)Unlabeled (output is not known)No predefined data
MethodAnalyze the relation between input and output. The output is predicted based on this relationAnalyze the input parameters to uncover hidden patterns. Output is predicted based on those patternsRandomly trialing a vast number of possible inputs, then comparing and grading their performance
Example Decision trees, support vector machines, neutral networks, k-nearest neighborsk-means clustering, archetype analysisQ-learning
Table 2 Machine learning applications used in different kidney transplantation areas
Kidney transplantation category
Machine learning methods used
Radiological evaluationNeural network, convolutional neural network, stacked autoencoders, Bayesian supervised classifier[4-9]
Pathological evaluationNeural network, Bayesian network, convolutional neural network, linear discriminant analysis, support vector machines, random forest, archetypal analysis[10-23]
Prediction of graft survivalNeural network, logistic regression, decision tree, random forest, support vector machines, LASSO, gradient boosting[24-39]
Optimizing the dose of immunosuppressionNeural network (multilayer perceptron, finite impulse response network, and the Elman recurrent network), adaptive-network-based fuzzy inference system, conditional inference trees, multiple linear regression, regression tree, multivariate adaptive regression splines, boosted regression tree, support vector regression, random forest regression, LASSO regression and Bayesian additive regression trees[40-46]
Diagnosis of rejectionNeural network, support vector machines, Bayesian interference[47-52]
Prediction of early graft functionNeural network, logistic regression, linear discriminant analysis, quadratic discriminant analysis, support vector machines, decision tree, random forest, gradient boosting, elastic net[3,53-57]