# Enhancing Feature Selection for Imbalanced Alzheimer's Disease Brain MRI Images by Random Forest
## 4 Results and Analysis
### Experimental Results and Evaluation
#### 4.3.3 Compared with Other Classification Models  
In this section, we compare the performance of our model with state-of-the-art methods, including SVM [60; 61], RNN [62], CNN [63], and KNN (RFE + PCA) [52]. In [61], the importance-based feature selection was adopted, and an SVM-based AD classification model was further established. [PERSON] et al. [61] developed shape descriptors to capture and quantify these changes and test their efficacy as imaging biomarkers for the  
Figure 8: The effect of the AUC under different feature selection algorithms. automatic classification of the following populations: MCI patients who progressed to AD, AD patients, and normal controls (NC). [PERSON] et al. [52] proposed a KNN model, based on RFE and PCA to identify AD, which used RFE and PCA for feature selection. [PERSON] et al. [62] and [PERSON] et al. [63] utilized different deep neural works for AD classification. In our study, the examination was conducted by splitting the data into 90-10 in the train-test split approach, and the experimental results are shown in Table 3.  
Table 3 presents the overall performance of the six methods. Since ADNI is an imbalanced dataset, from the experimental results and the data preprocessing method adopted, it can be found that designing an appropriate feature selection strategy for the characteristics of the dataset can effectively improve the performance of the model. For example, comparing the first and second methods in Table 3, the second method designs a more scientific feature selection algorithm, and therefore, its classification performance is better than the first method. For the second and third methods, regarding the NC-AD classification problem, the third method designs a more reasonable feature selection algorithm, in which RFE is applied with PCA to reduce the number of features; therefore, its performance is significantly better than the second method. Theoretically speaking, the fourth and fifth methods should obtain higher classification accuracy and AUC values compared with other methods; however, the experimental results are just the opposite. The main reason for this is closely related to the imbalance of the dataset and the quality of the learned features. The performance of the method proposed in this paper is significantly better than other methods for the NC-AD classification problem; regarding the MCI-AD classification \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Author** & **Method** & **Subject** & **Data** & **Target** & \begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} & **AUC** & **Train Time** \\ \hline [PERSON] et al. [60] & SVM & ADNI (550) & Clinical & MCI-AD & 78.8 & 88.0 & - \\ \hline [PERSON] et al. [61] & SVM & \begin{tabular}{c} ADNI (299) \\ ADNI (213) \\ \end{tabular} & Clinical & NC-AD & 80.54 & - & - \\ \cline{2-8} [PERSON] et al. [52] & \begin{tabular}{c} KNN \\ (RFE + PCA) \\ \end{tabular} & ADNI (382) & Clinical & NC-AD & 92.3 & 91.5 & - \\ \hline [PERSON] et al. [62] & RNN & ADNI (742) & Clinical & \begin{tabular}{c} NC-AD \\ MCI-AD \\ \end{tabular} & - & 90.3 & - \\ \cline{2-8} [PERSON] et al. [63] & CNN & ADNI (559) & Clinical/MRI & MCI-AD & 69.8 & 83.0 & - \\ \hline Proposed method & RF-AUC-Cor & \
\begin{tabular}{c} ADNI (382) \\ Algorithm \\ \end{tabular} &
\begin{tabular}{c} NC-AD \\ ADNI (270) \\ \end{tabular} & Clinical & MCI-AD & 84.5 & 85.7 & 2.60 s \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification performances of different models. Figure 9: The effect of accuracy under different feature selection algorithms. problem, its comprehensive performance (including classification accuracy, the AUC, and training time) can be ranked first. In this study, morphological features and texture features were extracted and fused, and feature selection was additionally applied to the dataset as a preprocessing step. On the one hand, it can reduce the classification performance degradation caused by data imbalance; on the other hand, it can additionally reduce the time complexity of model training.