## 3 Results and Discussion
The AD tertiary model demonstrates slightly worse performance than the AD binary and OD tertiary classification models, resulting in an accuracy of 94.84% as compared to the average accuracy of 96.47% and 98.31%, respectively. Such results can be expected, as only relatively minor changes in biochemical composition of blood serum are likely to be associated with AD progression from the mild stage to the moderate stage of the disease. With smaller sample sizes, one can expect larger variance between bootstrap results, however the observed variance of the performance parameters in this study, as determined by the calculated standard deviation, was very small thus indicating stability of the models. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Binary Model**} \\ \hline Predicted/Actual & AD & HC \\ \hline AD & 27 & 1 \\ HC & 0 & 12 \\ \hline \multicolumn{4}{c}{**Tertiary Model–AD**} \\ \hline Predicted/Actual & Mild AD & Moderate AD & HC \\ \hline Mild AD & 11 & 0 & 0 \\ Moderate AD & 1 & 14 & 0 \\ HC & 0 & 0 & 14 \\ \hline \multicolumn{4}{c}{**Tertiary Model–OD**} \\ \hline Predicted/Actual & AD & HC & OD \\ \hline AD & 28 & 0 & 0 \\ HC & 1 & 6 & 0 \\ OD & 0 & 0 & 13 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion table of the prediction results achieved for selected networks during the testing phase on the test datasets for all three models with spectra used as individual inputs. The results shown in Table 5 correspond to the performance parameters for samples at the individual spectrum level. Each spectrum receives a probability estimate for belonging to each class; the spectrum is then assigned to the class corresponding to the highest probability estimate. To determine the classification of each donor, overall, the donor is assigned to the class the majority of its spectra were assigned as. For the binary model and tertiary OD model, all donors were correctly classified. In the tertiary AD model, however, four donors were misclassified, each time occurring in a different bootstrap. The BLP cross-validation results shown in Table 5 exhibit good prediction results and prove that the model is not majorly overfit. Rigorous validation using the BLP scheme allows for overfitting to be avoided and ensures high quality of the resulting dataset. Further, accuracy is calculated in classification problems and is computed as the percentage of correctly classified inputs. The accuracy of the validation set is a good indicator of future classification generalization abilities. However, in order to monitor overfitting, the test and validation accuracies were compared, since an overfit model would be expected to perform much worse on the validation data. It is further expected that the test accuracy would ideally only be slightly higher or lower than the validation accuracy. This was confirmed by accuracy values achieved for binary AD, tertiary AD and tertiary OD models from the test set, which were 98% each for all models. These values are only slightly higher than the average accuracies of the BLP cross-validation results for the binary AD, tertiary AD and tertiary OD models, which were 96%, 95% and 98%, respectively (Table 5). Additionally, there have been a number of studies reported which use a very similar approach of Raman spectroscopy in combination with machine learning methods (specifically with ANN) for the analysis of the same size or smaller datasets than what is used in the current study. For example, the same approach was used to differentiate eleven patients with type 2 diabetes and nine controls (healthy subjects) from different anatomical locations, achieving 88.9-90.9% accuracy using a 10-fold cross-validation approach in donor-wise manner [65]. It should be noted that the study reported herein is designed as a proof-of-concept and more testing is necessary to obtain a strong understanding of the classification accuracy and generalizability of this framework.