# Enhancing Feature Selection for Imbalanced Alzheimer's Disease Brain MRI Images by Random Forest
## 4 Results and Analysis
### Experimental Results and Evaluation
#### 4.3.4 Discussion  
Based on MRI, the early lesion features of NC and MCI can be detected, and many studies have extracted brain features for the classification of NC, AD, and MCI. However, in the classification process, the class imbalance problem seriously affects the classification effect. Drawing on the existing solutions, this paper proposed a new feature extraction and fusion method to solve this problem. Meanwhile, considering the redundancy between the extracted features and further feature selection, only the key features that have a significant impact on the disease are screened out. In this way, the dimension of input features can be effectively reduced, the time cost of model training can be cut, and the classification prediction effect is better. Furthermore, the main factors affecting diagnosis can be found, and a reference for clinical practice can be provided.  
In the experiments with the feature extraction, firstly, the effect of the parameter \(K\) on the proposed algorithm was verified and then compared with other feature extraction methods, including PCA (in Section 4.3.2) and RFE + PCA (in Section 4.3.3). The results indicate that the proposed method can identify the minority class samples well and avoid the unsatisfactory classification effect caused by the majority class. This is similar to integrating oversampling technology and undersampling technology to preprocess samples. Therefore, the method proposed in this paper can deal well with the problem of unsatisfactory classification results caused by class imbalance.  
In our study, we have demonstrated that the random forest model can take MRI features and accurately predict NC-MCI, NC-AD, and MCI-AD problems. Our proposed classifier showed a superior performance compared to the competing SVM, KNN, RNN, and CNN. It is worth noting that this model has the highest accuracy and AUC value for the NC-AD group data (94.2% and 93.7%) classification problem, followed by MCI-AD (84.5% and 85.7%) and NC-MCI group data (83.8% and 87.6%) classification problems. Moreover, the training time of our model is the shortest, which is far lower than other models, of approximately 3.0 s only. Therefore, the proposed method can well predict the progression of disease from NC to AD, from MCI to AD, and from NC to MCI, which is critical to the study of why only certain NC and MCI patients develop AD, as well as to understanding the disease and developing accurate prognostic indicators.  
Furthermore, our original intention is to apply the proposed preprocessing method for class-imbalanced datasets to the whole field of medical research. Since most medical datasets contain a similar target class imbalance, our approach incorporates multi-type features to balance the weight of the minority class in the model. Currently, the proposed approach was only tested on binary classification problems; we plan to apply it to multi-class classification problems as well.  
One limitation of this study is that all patients were from the ADNI dataset. Although we split the dataset into multiple instances to validate its accuracy and AUC, no participant populations other than ADNI were tested. Taking other datasets as test objects for the model will be more helpful to verify its effectiveness, which is the goal of our future work.