## 5 Results
### Conclusions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author, Year** & **Cohort Data Base** & **Number of Subjects** & **Method Employed** & **Accuracy (\%)** \\ \hline [PERSON] et al., 2012 [23] & Zhejiang University. & PD (n = 19) vs. Normal (n = 26) & SVM, LOCCV & 86.9 \\ [PERSON] et al., 2014 [12] & Own data-base. & PD (n = 28) vs. Normal (n = 28) & PCA, SVM, VBM & 83.2 \\ \hline [PERSON] et al., 2015 [22] & Own data-base. & PD (n = 30) vs. Normal (n = 30) & SVM, ROI & 86.67 \\ \hline [PERSON] et al., 2016 [50] & PPMI. & PD (n = 374) vs. Normal (n = 169) & Joint feature-sample selection, RLDA & 81.9 \\ \hline [PERSON] et al., 2017 [51] & Own data-base. & PD (n = 27) vs. Normal (n = 38) & SVM, Functional connectome & 80.0 \\ \hline [PERSON] et al., 2018 [24] & PPMI. & PD (n = 123) vs. SWEDD (n = 29) & SVM, GDC,CSF, DSSM & 78.37 \\ \hline [PERSON] et al., 2018 [18] & PPMI. & PD (n = 378) vs. Normal (n = 169) & Random Forest, SVM & 93 \\ \hline [PERSON] et al., 2018 [25] & PPMI. FF-CIT SPECT & PD (n = 443) vs. Normal (n = 209) & SV, LOOCV, ROI, k-NN & 97.9 \\ \hline [PERSON] et al., 2019 [52] & UK Brain Bank. & PD (n = 39) vs. Normal (n = 40) & Artificial Neural Network & 79.9 \\ \hline [PERSON] et al., 2019 [19] & NIMHANS, India. & PD (n = 45) vs. Normal (n = 35) & CNNs & 80.00 \\ \hline [PERSON] et al., 2020 [20] & China Medical University. & IPD (n = 83) vs. MSA-P (n = 102) & Seven radiomic features, SVM & 86.2 \\ \hline [PERSON] et al., 2020 [53] & PPMI. & PD (n = 201) vs. Prodromal (n = 193) & Semi-supervised, LDA & 74 \\  & vs. Normal (n = 204) & Multi-view learning Clustering & & \\ \hline [PERSON] et al., 2020 [54] & Yonsei Univ. HealthSystem. & PD (n = 77) vs. Normal (n = 53) & SVM, Vol + ReHo + DegCen & 75.3 \\ \hline [PERSON] et al., 2020 [24] & PPMI. & (a) PD (n = 378) vs. SWEDD (n = 58) & (a) 73.3 (b) 97.4 \\ \hline [PERSON] et al., 2021 [55] & PPMI & PD (n = 348) vs. Normal (n = 159) & Seven classifiers & 93 \\  & & Haralick and Wrapper FS & & \\ \hline [PERSON] et al., 2021 [56] & U.Medical Center Hamburg. & \begin{tabular}{c} PD (n = 45) vs. PSP-RS (n = 20) \\ vs. Normal (n = 159) \\ \end{tabular} & \begin{tabular}{c} Regional morphology features \\ quantitative T2 values \\ diffusion-tensor \\ \end{tabular} & 95.1 \\ \hline Proposed method & PPMI. & PD (n = 450) vs. Normal (n = 258) vs. \\ Solution 1. C1 matter Figure 13c & SWEDD (n = 154) vs. Prodromal (n = 38) & 3D-DWT, MOE, SVM & 96.2 \\  & vs. GeneCohort (n = 17) & & \\ \hline Proposed method & \
\begin{tabular}{c} PD (n = 450) vs. Normal (n = 258) vs. vs. vs. \\ SWEDD (n = 154) vs. Prodromal (n = 38) \\ vs. GeneCohort (n = 17) \\ \end{tabular} &
\begin{tabular}{c} 3D-DWT, MOE, SVM \\ \end{tabular} & 95.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparative summary of different methodologies presented in the bibliography to predict PD from MRI. call it the first solution), has a total of 466 volumes of interest (231 for W matter and 235 for C1 matter). With this large number of VOIs, the classifier in the test phase obtained high accuracy: 97.0 % (70.8 % and 96.2 % using only the W and C1 matter, respectively). Another solution (the third of the Pareto front) with less complexity was analyzed. This solution selects 159 regions (73 for W matter and 86 for C1 matter). Using the 86 volumes of gray matter, the classifier, in the test phase, achieved an accuracy of 95.9 % in a multi-class problem. From the different solutions obtained in the Pareto Front, the high number of VOIs selected in the gray matter for the construction of the classifier can be obtained as a final conclusion. To the best of our knowledge, there are very few, if not almost none, references in the literature that perform 3-D feature extraction for VOI optimization in a multi-class classification related to [PERSON]'s disease. The results from the confusion matrices for the different solutions of the analyzed MOE confirm that feature extraction using 3D-DWT, together with feature reduction using mRMR and PCA, are powerful tools for characterizing brain regions.