## 3 Results and Discussion
This is an interesting result, as it allows us to foresee the translation possibility of the FTIR technique and machine learning models in medical diagnostics. In fact, medical practice involves samples (cells, tissues) located on glass supports, which, from an optical point of view, are unusable in LWR due to the absorption of IR radiation by the glass in this spectral range. To evaluate the eventual presence of overfitting and the loss of the ability to generalize the model predictions, we performed a re-training of the data after randomly varying the class labels of the spectral features from the calibration set. In this case, a good performance of the classification models would have been an index of the presence of overfitting due to spurious information unrelated to inter-class differences [47; 52]. Conversely, the poor performance of the models applied to randomized class data indicate that the models applied to non-randomized original data assess differences which are actually related to different classes. The obtained results are shown in Table 3. It was reported that the obtained accuracy was close to 50% for most of the models. This low accuracy (close to chance) suggests a low degree of overfitting in the training step of the original data, and, consequently, it also suggests that the results shown in Table 2 are actually due to interclass differences. However, we noted that a relatively high sensitivity value was obtained from the SVM model. This indicates a tendency of the SVM model to overestimate the positivity of the data, i.e., the belonging of the spectral data to cancerous cells. Therefore, after training the spectral data, it was found that the algorithm with the best performance regarding accuracy, sensitivity, and specificity values was the NN model. Hence, it is suitable for the identification of cancerous colon cells and their discrimination from healthy cells. The other models also showed good performances, even if inferior to that of the NN algorithm. The SVM model should be excluded, although it yielded an excellent performance regarding sensitivity when it was applied to randomized data. To further assess the ability of machine learning models to classify colon cells into two types, i.e., healthy and cancerous, we tested the machine learning algorithms on a set of unknown FTIR spectra. The obtained values of the performance parameters are reported \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Algorithm** & **Accuracy LWR** & **Accuracy HWR** & **Sensitivity** & **Sensitivity** & **Specificity** & **Specificity** \\
**(Randomized Data)** & **(\%)** & **(\%)** & **LWR (\%)** & **HWR (\%)** & **LWR (\%)** & **HWR (\%)** \\ \hline kNN & 45.5 & 51.9 & 57.1 & 52.4 & 31.4 & 51.4 \\ \hline LR & 54.5 & 50.6 & 64.3 & 64.3 & 42.9 & 34.3 \\ \hline CT & 51.9 & 53.2 & 61.9 & 59.5 & 40.0 & 45.7 \\ \hline CN2-RI & 49.4 & 46.8 & 54.8 & 50.0 & 42.9 & 42.9 \\ \hline SVM & 51.9 & 54.5 & 73.8 & 78.6 & 25.7 & 25.7 \\ \hline NN & 50.6 & 57.1 & 50.0 & 64.3 & 51.4 & 48.6 \\ \hline \end{tabular}
\end{table} 
Table 3: Performance parameters obtained by applying the investigated classification algorithms to the calibration set of FTIR spectra of healthy and cancerous colon cells, measured in the low-wavenumber range (LWR) and high-wavenumber range (HWR), after randomization of the class labels. in Supplementary Materials Table S1 and Figure S1. In particular, the values of accuracy, sensitivity, and specificity obtained from the NN algorithm were excellent (100%) for both spectral ranges, and were comparable to those of Table 2. This is a further remark that rules out the presence of overfitting in the spectroscopic data and ensures that the developed NN classification model is able to generalize the results to unknown new data.