“…It appears that the majority of the studies have an interesting average rate of classification precision (31 analyses obtain a precision or sensitivity higher than 85 percent [ 77 , 78 , 88 , 94 ]). It should also be noted that certain types of classifiers are recurrent, notably Random Forest (RF) (11 studies [ 83 , 85 , 100 ]) and Decision Trees (DT) (eight studies [ 38 , 61 ]), Support Vector Machine (SVM) (12 studies [ 43 , 76 , 84 , 94 ]), Bayesian approaches (Naive Bayes (NB)) (three studies [ 64 , 76 , 94 ]), K-Nearest Neighbor (KNN) (four studies [ 83 , 84 , 93 , 94 ]) and Neural Network (NN) (four studies [ 15 , 43 , 76 , 108 ] with notably Multi-Layer Perceptron (MLP)). Other classifiers (K-Means (K-M), Linear Regression (LR)) are also put in place in some studies but in less frequent manners and other methods and ancillary methods can also be implemented (Hidden Markov Model (HMM), Receiving Operating Characteristics (ROC), and Adaptation Detection Chain (ADC)).…”