## 3 Results and Discussion
Also, the SVM method was successfully used for the classification of spectra with good accuracy: [PERSON] et al. used the ATR-FTIR tool associated with the SVM classifier in order to detect modifications to salivary components to be used as biomarkers for the diagnosis of type 2 diabetes mellitus with an accuracy of 87% [48]. The SVM algorithm was also able to distinguish the Raman spectra of extracellular vesicles in the serum of cancer patients from those of healthy controls with a classification accuracy of 100% when reduced to the spectral frequency range from 1800 to 1940 cm\({}^{-1}\), although the accuracy values significantly decreased to 67% and 57% when the complete Raman spectrum and FTIR spectrum, respectively, were used [49]. Good classification performances were also reported for the kNN model. In particular, accuracy values from 79% to 97% were reported for several kNN-based models in the classification of FTIR spectra measured for serum samples collected from healthy and ductal carcinoma patients [50]. The KNN classification model was also successfully applied to Raman spectra of tissue samples to diagnose lung cancer with an accuracy value of 97%, although it decreased to 90% for the discrimination of adenocarcinoma from squamous carcinoma samples [51]. Therefore, our results are in good agreement with those reported by other authors for similar models applied to the classification of vibrational spectra. In fact, the SVM, NN, and kNN algorithms are characterized by high sensitivity values (from 97.6% to 100.0%) in both spectral ranges. Such values indicate a low missed diagnosis rate and, consequently, a reduced risk that the disease will not be diagnosed (and, therefore, the patient will not be treated and may progress to a more severe condition). As for specificity values, the SVM and NN methods performed better than the kNN and other models, particularly in the LWR, where specificity values of 97.1% to 100.0% were obtained, respectively. These values revealed a low misdiagnosis rate and, consequently, a low probability of patients receiving unnecessary treatments. Instead, the specificity value of the kNN algorithm was 94.3% in the HWR and even lower in the LWR (82.9%). Therefore, it can be deduced that the reduced accuracy of the kNN and other models with \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Algorithm** & **Accuracy LWR** & **Accuracy HWR** & **Sensitivity** & **Sensitivity** & **Specificity** & **Specificity** \\
**(Original Data)** & **(\%)** & **(\%)** & **LWR (\%)** & **HWR (\%)** & **LWR (\%)** & **HWR (\%)** \\ \hline kNN & 90.9 & 97.7 & 97.6 & 100.0 & 82.9 & 94.3 \\ \hline LR & 94.8 & 93.5 & 95.2 & 95.2 & 94.3 & 91.4 \\ \hline CT & 87.0 & 94.8 & 88.1 & 95.2 & 85.7 & 94.3 \\ \hline CN2-RI & 90.9 & 89.6 & 95.2 & 95.2 & 85.7 & 82.9 \\ \hline SVM & 100.0 & 97.4 & 100.0 & 97.6 & 100.0 & 97.1 \\ \hline NN & 98.7 & 98.7 & 100.0 & 100.0 & 100.0 & 97.1 \\ \hline \end{tabular}
\end{table} 
Table 2: Performance parameters obtained by applying the investigated classification algorithms to the original calibration set of FTIR spectra of healthy and cancerous colon cells, measured in the low-wavenumber range (LWR) and high-wavenumber range (HWR). respect to SVM and NN in the LWR is mainly related to the specificity values. Indeed, the specificity values are slightly lower than the sensitivity values for all investigated models. By considering that, in our case, the specificity values depend on the ratio between the FTIR spectra evaluated as belonging to healthy cells with respect to those actually belonging to healthy cells, the lower specificity value is probably related to the greater dispersion of the absorption values in the healthy cell spectra compared to the cancerous spectra (see standard deviation values in Figure 1). Overall, the values of the performance parameters reported in Table 2 suggest that the HWR can be reliably used to train classification models for colon cancer diagnosis. Nonetheless, it is characterized by a minor number of spectral features compared to the LWR.