# Classification of Alzheimer's Disease Based on White Matter Connectivity Network
## 3 Results and Discussion
### Results and Discussion  
Based on the results of the RFE feature selection, the SVM-RBF classification algorithm was used to classify two subject groups of AD and MCI, and NC and MCI, and Table 4 shows the results. Figure 8 shows the ROC curves for each group.  
Figure 6: Cross-validation scores of eigenvectors between MCI and AD groups.  
Figure 7: Characteristics of the RFE selection.  
In the classification of AD, MCI, and NC, Table 5 demonstrates the relevant studies on DTI-based classification tasks in recent years using different classification methods. [PERSON] et al. [40] fused the ROI of structural MRI and DTI, extracted the MD of the hippocampus (HIP), and classified AD, MCI, and NC by using a convolutional neural network (CNN). The experiment only used an evaluation index of ACC, and it was low, with the ACC of AD/MCI being 80% and MCI/NC being 63%. [PERSON] et al. [9] selected the FA value of bilateral para-hippocampal gyrus and used linear SVM to classify AD, MCI, and NC. Although the ACC of the AD/MCI and MCI/NC classification was high, both of which were 90%, the evaluation index of the experiment was relatively single and the dataset was small, which made it easy to overfit. [PERSON] et al. [41] selected the MD values of the HIP and the entrepreneurial cortex (EC), and classified MCI and NC using a CNN. Although ACC, SPE, and AUC were selected as evaluation indexes, the results were not very good. The ACC was 71.1%, the SPE was 81.8%, and the AUC was 0.68, which were lower than the results of our study. [PERSON] et al. [42] also used SVM-RFE to classify MCI and NC. Although the selected evaluation indexes were the same as ours, all the indexes were lower than our results, with an ACC of 79.8%, SEN of 84.1%, SPE of 73.8%, and AUC of 0.901. The reason may be that only the FA value related to the HIP was selected as the classification feature, and the influence of other fiber bundles in the development of AD was ignored. [PERSON] et al. [43] classified AD, MCI, and NC according to the DTI features in superficial WM, extracted the FA, MD, and RD values of the whole brain, and used quadratic SVM as the classifier. Although the SPE of AD/MCI and the SEN of MCI/NC were higher than our results, which were 86.3% and 94.4%, respectively, the other indexes were lower than our results, and the experimental data were also less than ours. In this study, we calculated the FA values of the whole brain WM, and selected the nodes with significant differences as the classification features using statistical tests and RFE. The ACC for AD/MCI was 89.29%, the SEN was 92.86%, the SPE was 85.71%, and the AUC was 0.68%, and the AUC was 0.68%, respectively. The results of the AD/MCI and NC classification are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Indictors** & **AD vs. MCI** & **MCI vs. NC** \\ \hline ACC & 89.29\% & 91.18\% \\ SEN & 92.86\% & 92.31\% \\ SPE & 85.71\% & 90.48\% \\ AUC & 0.94 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification results of SVM-RBF.  
Figure 8: ROC curve.  
0.94; for MCI/NC, the ACC was 91.18%, the SEN was 92.31%, the SPE was 90.48%, and the AUC was 0.95. Compared with these studies, our method has a competitive advantage in classification performance.