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. |