## 3 Materials and Method
### Feature Extraction  
The dataset utilized in this study faced the challenge of high dimensionality within its feature space, posing potential issues for subsequent analyses. To address this challenge, effective feature selection and feature extraction methods were employed to mitigate the inclusion of irrelevant features and reduce the dimensionality of the feature space. Among the commonly employed techniques for this purpose, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were selected as the preferred methods in this study.  
PCA is a straightforward and widely used dimensionality reduction technique. Its objective is to reduce the dimensionality of a high-dimensional dataset by eliminating irrelevant features while preserving the underlying structure of the data. It is crucial to determine the number of principal components in PCA. The optimal number of components, denoted  
Figure 3: Donor weight data distribution graph (histogram) after data preprocessing.  
Figure 2: Donor weight histogram graph in the original dataset.  
as p, should be selected to best represent the data [18]. In this study, the optimal value of p was determined as 80. As a result, the original dataset with dimensions 109 \(\times\) 188 was reduced to a dataset with dimensions 109 \(\times\) 80.  
In contrast to PCA, LDA takes into account the class membership information when reducing dimensionality. LDA is a dimensionality reduction technique commonly used in classification problems. It aims to find the optimal set of projection vectors that map the original data to a lower-dimensional feature space. LDA effectively handles situations where class frequencies are imbalanced and performances are random [19; 20]. Given the presence of numerous features in the dataset, the'svd' parameter was used, which automatically adjusts the size of the data. Consequently, the original dataset with dimensions 109 \(\times\) 188 was reduced to a dataset with dimensions 109 \(\times\) 2.  
To understand the effectiveness of the features obtained through PCA and LDA, a feature importance analysis was conducted. This analysis was used to enhance model performance and provide better explanations. Among feature importance methods, the Shapley Additive Explanations (SHAP) method was used, which is a powerful and commonly used technique in ML models. It is also model-agnostic. Figure 4 illustrates the feature importance of the dataset on the Logistic Regression (LR) model without any feature extraction method as an example. In the graph, the bars located on the positive (upper) and negative (lower) axes indicate the impact of each feature on the model. Features on the positive axis contribute positively to the model, while those on the negative axis contribute negatively. This can also be interpreted as positive and negative effects on the model's output. Features close to zero have no meaningful impact on model performance.  
Due to the large number of features in the dataset and the fact that many of them have values close to zero, feature selection was performed. Feature extraction methods were used to remove unnecessary features and optimize the model, making it more efficient and faster. Figure 5a shows the feature importance on the LR model after feature extraction using the PCA method. Figure 5b illustrates the feature importance on the LR model after feature extraction using the LDA method.  
Figure 4: Feature importance on LR model without using feature extraction method.