“…k-means clustering [14] Divides a number of data points into a number of clusters based on the nearest mean k-nearest neighbors (kNN) [10,22,24,27,29,32] Assigns data patterns to a class on the basis of the distance to the training patterns of a certain class Multilayer perceptron (MLP) [9,22] Trains on a set of input data patterns to predict/classify the output class Random forest (RF) [14,15,28,29,31,32] Builds and merges multiple decision trees to provide a more accurate prediction Regression: kernel ridge regression (KRR), [30]; elastic net (EN) [23,28]; generalized linear mixed-models (GLMMs) based on repeated data points, Lasso [15,24]; least square (LS) [28]; linear regression (LiR) [27,33]; logistic regression (LoR) [10,15,29,31,32]; ridge regression (RR) [28] Predicts the probability of agreement using continuous data points Support vector machine (SVM) [9, 12, 15, 21, 22, 25-27, 29, 32, 34] Creates a hyperplane to separate two classes. The hyperplane is found by optimizing a cost function Multi-subject dictionary learning (MSDL) [16] It is a feature learning method where a training example is represented as a linear combination of basic functions, and is assumed to be a sparse matrix medical hypotheses, etc.…”