###### Abstract
If the decision rules do not allow for a complete separation of the whole dataset into classes, internal nodes are formed based on other spectral features. Further branches originating from the internal nodes report further decision rules, which allow us to continue the partition of the unclassified data until all data are separated according to the proper classes. The leaves are the terminal structures and represent the classification results of the data set [30]. Diagnostic models based on FTIR spectra classified by CT achieved an accuracy of 99.24% for discrimination between hepatocellular carcinoma and normal tissue [31]. Also, Raman spectra of neoplastic and normal nasopharyngeal cell lines were classified by CT with 98.5% accuracy [32]. SVM is a binary classification algorithm based on the optimization of separation of observation (i.e., spectra) belonging to different classes by finding hyperplanes, in a transformed space of the variables, that maximize the margin from the boundaries of observations belonging to the two classes. The optimal hyperplanes are identified during the training step and a criterion is established to separate the observations belonging to different classes, located on opposite sides of the hyperplanes (for example, the values \(-1\) and \(+1\) are used to encode the observations belonging to different classes). Then, an unknown observation is projected onto these hyperplanes and classified according to the criterion defined in the training step [18; 33]. Recently, urine surface-enhanced Raman spectroscopy combined with the SVM algorithm enabled the diagnosis of liver cirrhosis and hepatocellular carcinoma with accuracy levels of 85.9% and 84.8%, respectively [34]. Also, FTIR of serum samples, in conjunction with the SVM algorithm, proved to be a sensitive tool for the detection of HCV infection and to assess the non-cirrhotic/cirrhotic status of patients [35]. The kNN algorithm is a classification method for estimating the likelihood that a sample will belong to one group or another based on which group the samples nearest to it belong to. The first step is the proper selection of the k value, because kNN attempts to predict the correct class for an unknown sample by calculating the distance between the sample and all the training samples, and, successively, selecting the k number of samples which are closest to the unknown sample. Then, the unknown sample is assigned to the prevalent class among the classes of the k neighbours. Raman spectroscopy of serum samples, coupled to the kNN classification model, has been used as a diagnostic technique for endometriosis [36]. The kNN algorithm has also been used for the classification of white blood cells in different types of acute myeloid leukaemia according to cells' morphological characteristics [37]. The NN is a classification algorithm whose aim is to search for relationships among samples in a dataset through a process that mimics the way in which the human brain operates. The NN method is based on many artificial nodes (corresponding to neuronsin the human brain) arranged in layers: each node is connected to all other nodes in the adjacent layers. Such layers are organized into input layers, output layers, and (one or more) hidden layers. The variables x of a dataset feed the input layer. All these variables are fed as input to every node in the hidden layer, where different linear combinations of the variables are built and a nonlinear function is applied to obtain new variables z, which depend on the original variables. This process occurs inside the hidden layer, where each neuron takes several variables x as inputs and produces one single output z. Finally, the new variables z can be used in different ways to obtain the final output y, which is the codified target variable [38]. NN-based algorithms applied to vibrational spectra data have been often used to solve classification problems in medicine [30; 39; 40].