However, there exists a huge research gap for the implementation of other DM and ML methods for big data reduction that include supervised, unsupervised, semi-supervised, and hierarchical deep learning models [85].
Any program should include supervised clinical practice.
Secondly, the methods they selected included supervised and unsupervised approaches which, in our view, could be unfair for the unsupervised ones.
While the supervised methods include Bayes classifiers and artificial neural networks, unsupervised methods include k-means, fuzzy clustering techniques[10, 11], and statistical methods such as Markov random fields (MRF).
Some methods include drop box or supervised collection sites, mail-back programs, or disposal through waste disposal contractors.
These methods include lexical-based approaches methods and supervised machine learning methods.
Options for classification using machine learning methods include: unsupervised methods such as clustering and blind signal separation, or supervised methods such as artificial neural networks, support vector machines, nearest neighbor methods, Naive Bayes, decision trees, among others [ 6].
Bøvelstad et al. [ 36, 37] performed a comparative study of six Cox regression-based survival prediction methods, including univariate selection, supervised principal component analysis, partial least squares regression, ridge regression and lasso, finding that ridge regression presented the best overall performance.
Other methods include food deprivation.
