# Feature Selection and Transfer Learning for Alzheimer's Disease Clinical Diagnosis
## 4 Experiments
### The Efficiency of TrAdaboost Algorithm  
In this study, we tested and verified that the transfer learning algorithm had higher accuracy and stability than some classic algorithms. To keep the balance of sample sizes for better classification performance, we divided the AD, MCI, and NC data into small groups, as shown in Table 4. Then, the classification accuracy was calculated based on AD as training data and MCI as testing data with different grouping combinations. To have a better understanding of the efficiency of the TrAdaboost algorithm, we also applied classic algorithms, including SVM (support vector machine), KNN (K-nearest neighbor), ITML (information theory metric learning), and Linear-MSVM (linear metric based support vector machine). The accuracy of each algorithm was compared and studied with MCI1/NC3 as training data in target domain (Tartrain) whose sample size set at 10/10; AD1/NC1, AD2/NC1, AD3/NC1, AD4/NC1, and AD/NC1 \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Group1** & **Group2** & **Group3** & **Group4** & **Group5** \\ \hline Accuracy & 0.854 & 0.778 & 0.831 & 0.804 & 0.804 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Lab2 The accuracy of TrAdaboost algorithm on different Src groups. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **10** & **15** & **20** & **25** & **30** \\ \hline KNN & 0.425 & 0.481 & 0.488 & 0.475 & 0.487 \\ SVM & 0.7407 & 0.752 & 0.768 & 0.721 & 0.813 \\ ITML & 0.489 & 0.623 & 0.548 & 0.729 & 0.482 \\ Linear MSVM & 0.721 & 0.753 & 0.735 & 0.741 & 0.733 \\ TrAdaboost & 0.804 & 0.804 & 0.828 & 0.837 & 0.843 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracy of test algorithms in different Tar-test sample sizes. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Training Data** & **Testing Data** & \multicolumn{3}{c}{**Results**} \\ \hline AD/NC & AD1/NC1 & AD2/NC2 & Acc & Sen & Spc \\ \hline AD1/NC1(2/2) & AD2/NC2(16/16) & 0.9375 & 0.875 & 1 \\ AD1/NC1(4/4) & AD2/NC2(14/14) & 0.928571 & 0.8571 & 1 \\ AD (177) & AD1/NC1(6/6) & AD2/NC2(12/12) & 0.916667 & 0.8333 & 1 \\ NC (85) & AD1/NC1(8/8) & AD2/NC2(10/10) & 0.9 & 0.8 & 1 \\ AD1/NC1(10/10) & AD2/NC2(8/8) & 0.9375 & 0.875 & 1 \\ AD1/NC1(12/12) & AD2/NC2(6/6) & 0.916667 & 0.833333 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracy (ACC), Sensitivity (SEN), and Specificity (SPE) results. as training data in source domain (Src), respectively. As shown in Table 5 and Figure 6a, TrAdaboost algorithm clearly outperformed other compared algorithms, with 80.44% as compared with 42.5% in KNN, 74.07% in SVM, 56.3% in ITML, and 72.1% in Linear-MSVM. The findings in Figure 6b-e indicated that accuracy of TrAdaboost algorithm fluctuated mostly when the TarTrain MCI1/NC1 was 15/15 and AD1/NC1, AD2/NC1, AD3/NC1, AD4/NC1, and AD/NC1 were the source domain training data (Src). With the Formulas (8), (9) and data shown in Table 6, the ratetop and ratebot was calculated as 5.77% and 3.25% respectively. It shows that using AD/NC1 as Src has no significant difference with other Src groups, i.e., AD1/NC1, AD2/NC1, AD3/NC1, and AD4/NC1. Since the test result would be more stable with larger sample size, we no longer considered other Src groups, just used AD/NC1 as Src in the experiments of selected feature and local hospital subjects. This study also examined the classification accuracy of different algorithms with different Tartrain sample size. The results in Figure 6a-e and Table 7 showed that when Tartrain MCI1/NC3 was 15/15 with Src was AD/NC1, the classification accuracy of SVM, KNN, ITML, Linear-MSVM, and TrAdaboost algorithm were 0.7523, 0.4808, 0.623077, 0.753036, and 0.804615, respectively. TrAdaboost algorithm achieved the highest accuracy in all tested methods. Similarly, our algorithm had the highest accuracy among all the tested methods, as the sample size of MCI1/NC3 was altered to 20/20, 25/25, and 30/30 (Table 7). The data in Table 7 and Formula (10) were used to calculate the change rate of accuracy.