# Classification of Alzheimer's Disease Based on White Matter Connectivity Network
## 4 Conclusions  
DTI is currently the only noninvasive technique that can display the microstructure of WM fiber bundles in the living brain, and previous studies have shown that the application of DTI technology combined with machine learning has great potential in the classification of AD, MCI, and NC. In this study, the white matter connectivity network of AD, MCI, and NC was constructed based on DTI data. Statistical tests and RFE were used to select the features of the nodes between the groups, and the FA values of the nodes with significant differences were used as the combination of classification features. The SVM-RBF algorithm was utilized to categorize AD, MCI, and NC. The experiment has achieved good classification results, and it was also found that the changes in the FA values of the fiber bundles in the cortical visual area, cortical motor area, and cortical sensory area are more significant during the progress of AD, especially the WM degeneration in the cortical motor area, which may play a decisive role in AD progression. This will provide some support and help for a more in-depth study of the pathophysiological mechanisms of AD development and new targets for its diagnosis and treatment, which are of great clinical significance for an early and timely intervention to slow down the progression of AD. However, there are still some limitations in this study. This study only discusses the differences in the FA values among AD, MCI, and NC, and does not analyze and compare the parameters such as MD and RD. After this, we will study other parameters of DTI and compare them with the results of this study. At the same time, the current study only uses the neuroimaging technology of DTI, in the future, we will try to carry out pattern recognition and intelligent diagnosis by combining other neuroimaging technologies. Based on the existing research results, we will improve the generalization ability by using deep learning technology to further promote the early diagnosis of AD and MCI. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Studies**} & \multicolumn{3}{c}{**Subjects**} & \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{**Feature**} & \multicolumn{2}{c}{**Performance**} \\ \cline{2-3} \cline{5-8}  & **AD** & & & & **MCI** & **NC** & **AD/MCI** & **MCI/NC** \\ \hline [PERSON] et al. [40] & 48 & 108 & 58 & CNN & HIP-related MD & ACC: 80\% & ACC: 63\% \\ \hline [PERSON] et al. [9] & 15 & 15 & 15 & Linear SVM & \begin{tabular}{c} Bilateral \\ parahippocampal \\ gyrus FA \\ \end{tabular} & ACC: 90\% & ACC: 90\% \\ \hline [PERSON] et al. [41] & / & 106 & 185 & CNN & \begin{tabular}{c} HIP-related and \\ EC-related MD \\ \end{tabular} & / & \begin{tabular}{c} ACC: 71.1\% \\ SPE: 81.8\% \\ AUC: 0.68 \\ \end{tabular} \\ \hline [PERSON] et al. [42] & / & 42 & 54 & SVM-RFE & HIP-related FA & / & \begin{tabular}{c} ACC: 79.8\% \\ SEN: 84.1\% \\ SPE: 73.8\% \\ AUC: 0.901 \\ \end{tabular} \\ \hline [PERSON] et al. [43] & 24 & 24 & 24 & Quadratic SVM & Whole brain FA, MD, RD & \begin{tabular}{c} ACC: 83.3\% \\ SEN: 80.7\% \\ SPE: 86.3\% \\ AUC: 0.93 \\ \end{tabular} & \begin{tabular}{c} ACC: 83.3\% \\ SEN: 80.7\% \\ SPE: 76.6\% \\ AUC: 0.88 \\ \end{tabular} \\ \hline Our study & 38 & 46 & 56 & SVM-RFE & Whole brain FA & \
\begin{tabular}{c} ACC: 89.29\% \\ SEN: 92.86\% \\ SEN: 85.71\% \\ AUC: 0.94 \\ \end{tabular} &
\begin{tabular}{c} ACC: 91.8\% \\ SEN: 92.31\% \\ SPE: 90.48\% \\ AUC: 0.95 \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 5: Comparison with the previous studies. **Author Contributions:** Conceptualization, Y.X. and X.Y. ; methodology, Y.X. ; software, Y.X. ; validation, Y.X., X.Y. and Z.L. ; investigation, L.L., Z.F. and J.Z. ; data curation, Y.X. ; writing--original draft preparation, Y.X. ; writing--review and editing, X.Y. and Z.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable.