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. 2022 Oct 26;93(5):e2022297. doi: 10.23750/abm.v93i5.13626

Table 2.

The most used algorithms of machine learning in medical research. Abbreviations: BIRCH, balanced iterative reducing and clustering using hierarchies.

Algorithm Brief Description
Supervised
Support Vector Machine (SVM) It divides the learning data into classes, enlarging the distance from all points. Used for both classification and regression problems.
Linear Regression Allow estimating the value of a variable that depends on many others.
Multinomial Logistic Regression (MLR) Classification approach that generalizes logistic regression (a binary regression model that uses the logistic function to model the binary dependence) with a multiclass task. It can be viewed as the modality of assignment to a definite class, adopting the one that ensures the best probability.
Bayesian networks (BN) Graphic model indicates a probability distribution in a set of arbitrary variables. The algorithm encompasses a probability distribution of the variables and a graph illustrating the dependencies between variables.
K-nearest neighbors (kNN) It classifies a point based on the known classification of other points (votes of the closest k neighbors).
Restricted Boltzmann machine (RBM) Graphic model with proportional reciprocity between observable and hidden variables. Links between elements of the same layer are not permitted to facilitate the learning mechanism.
Relevance vector machine (RVM) and Gaussian process (GP) Bayesian extensions of the SVM algorithm. The assessment is provided on the probability of being included in a class.
Decision Tree (DT) Graphic model (rule-based model) shows the decision points as branching and the applicable prediction in terms of end-nodes or leaves.
Unsupervised
Fuzzy C Means Flat/Partitioning-based algorithm that assigns elements to each data point related to each cluster center. It is established on the distance between the the cluster’s center and the data point.
BIRCH A hierarchical-based algorithm that works over large data sets, requiring a single database scan.
K-means Clustering algorithm splits a set of points (with no external classification) into K sets (clusters). The points in a cluster are disposed near each other. It is one of the various possible methods for solving the k-NN problem.
Reinforcement algorithms
Markov decision process It dissects the environment (where the learner, or agent, interacts) as a grid by dividing it into states, actions, models/transition models, and rewards. The solution is a policy (rewards combinations) and the objective is to find the optimal approach.
Q learning The value-based approach of supplying information to inform which action an agent should take.