Artificial intelligence in liver transplantation: Opportunities and challenges

Table 1 Types of machine learning

Type of machine learning	Methods & data used	Applications
Supervised learning	Labeled data	Predicts known outcome, e.g., SVM
Unsupervised learning	Unlabeled data	Identifies hidden pattern in the data, e.g., K-means, clustering
Semi supervised learning	Hybrid-less labeled & more unlabeled data	Useful when obtaining labeled data is expensive or time consuming
Reinforcement learning	Learning through interaction and feedback (based on evolutionary concept of human behavior of reward and punishment)	Useful in robotics and natural language processing
Ensemble learning	Multiple base models combined to develop more accurate predictive model	Enhance accuracy and reduce overfitting
Deep learning	Based on Artificial Neuronal Network inspired by the human brain neural networks	Useful in handling unlabeled data, e.g., image recognition, natural language processing

SVM: Support vector machines.