## 3 Results and Discussion
Lastly, the absorption intensity values of the DNA-related peaks, shown in Figure 3e,f, largely overlapped for the two cell types, especially as for the peak at 1236 cm\({}^{-1}\), for which there was not a statistically significant difference between the distributions of intensity values of the group of healthy cells compared to that of cancerous cells. Therefore, in our opinion, this univariate analysis is not reliable enough to discriminate cancerous cells from healthy ones and, consequently, its use in the clinical diagnostic field remains limited. On the contrary, it is interesting to evaluate the effectiveness of multivariate analysis methods in the discrimination between the two types of cells. Therefore, we evaluated the results obtained from several classification algorithms for each of the two wavenumber ranges. In particular, six classification algorithms (kNN, LR, CT, CN2-RI, SVM, and NN) were trained. The spectral features used for the classification were manually selected as corresponding to the spectral positions of absorption peaks, which were identified in Figure 2 according to the negative minima of second derivative signals of the mean spectra. For each algorithm, the values of the parameters used to control the learning process were optimized, as described as follows:  
* CN2-RI: ordered rules, exclusive covering, entropy evaluation with beam width equal to 5 for rule searching, minimum rule coverage of one, and maximum rule length equal to 5;
* LR: non-regularization type;
* CT: a binary tree, with minimum two samples per leaf; subsets were not split if they contained fewer than five samples and the maximal tree depth was equal to 100;
* SVM: radial basis function (RBF) kernel, SVM with cost 1.0 and regression loss epsilon 0.1, tolerance 0.001, and maximum 100 iterations;
* kNN: the number of neighbours equal to four for LWR and two for HWr, by using an Euclidean metric and weights by distances;
* NN: 95 neurons in the hidden layer, ReLu activation, Adam solver, and 300 maximum iterations. The performance obtained by the mentioned models during the training step of the original calibration data is reported in Table 2. Although all machine learning techniques achieved good classification results, accuracy values greater than 95% were obtained by SVM, NN, and kNN (for the latter, as far as the HWR was concerned). In particular, these three models were characterized by accuracy values from 97.4% to 98.7% for the HWR, whereas SVM and NN showed better performances than kNN for the LWR (100% and 98.7% for the former, respectively, and 90.9% for the latter). The sensitivity and specificity values reported in Table 2 were calculated by considering that the target of machine learning techniques is to detect cancerous cells: therefore, healthy cells were considered as negative and cancerous cells as positive. Comparative analyses of machine learning algorithms are becoming increasingly popular in the use of spectroscopic data for the purpose of classifying biological samples. In many of these comparative studies, neural-network-based techniques usually achieve excellent classification performances. [PERSON] et al. compared 10 supervised machine learning methods on 2752 surface-enhanced Raman spectra (SERS) from 117 _Staphylococcus_ strains belonging to 9 clinically important _Staphylococcus_ species. This investigation was conducted in order to test the capacities of different machine learning methods for rapid bacterial differentiation and accurate prediction. They found that convolutional neural network (CNN) performed better with respect to other supervised machine learning methods in predicting _Staphylococcus_ species via SERS spectra, achieving an accuracy value of 98.21% [46]. Recently, [PERSON] et al. applied many machine learning techniques for the classification of Candida species according to Raman spectra: they also found that the CNN algorithm achieved the greatest accuracy (91%) in the classification of a spectral dataset according to 11 classes [47].