Copyright
©The Author(s) 2021.
World J Gastroenterol. Aug 28, 2021; 27(32): 5306-5321
Published online Aug 28, 2021. doi: 10.3748/wjg.v27.i32.5306
Published online Aug 28, 2021. doi: 10.3748/wjg.v27.i32.5306
Table 1 Overview of the most widely adopted machine learning algorithms in rectal cancer imaging
Algorithm name | Description |
Random forest | An ensemble method that combines multiple decision trees (a class of predictive learning models used in supervised ML) to obtain more accurate results for classification and regression tasks |
Support vector machine | A linear approach used mainly for classification problems with the aim to find the best hyper plane which most accurately separate input data into two classes |
Logistic regression | A classifier used to obtain the best fitting model for the relationship between multiple predictor variables and a dichotomous outcome |
LASSO | A regularized regression method that performs both variable selection and regularization in order to optimally fit the resulting generalized statistical model |
Naive Bayes | A classifier relying on the Bayes Theorem to model the probability of an outcome based on the strong (naive) independence assumptions between the features data |
Quadratic discriminant analysis | A subtype of Dimensionality Reduction Algorithms that turn high-dimensional data into to low-dimensional data retaining the most significant features of original data for the prediction of the class label |
ANN | A subgroup of ML composed of neuronal-like multi-layered networks allowing to automatically extract features without prior labelling and perform complex operations |
CNN | As subset of ANN containing multiple computational hidden layers that filter and compute high-dimensional data to enhance the learning of high-level tasks (deep learning) |
- Citation: Stanzione A, Verde F, Romeo V, Boccadifuoco F, Mainenti PP, Maurea S. Radiomics and machine learning applications in rectal cancer: Current update and future perspectives. World J Gastroenterol 2021; 27(32): 5306-5321
- URL: https://www.wjgnet.com/1007-9327/full/v27/i32/5306.htm
- DOI: https://dx.doi.org/10.3748/wjg.v27.i32.5306