## 2 Bibliographic Study
### Sampling Methods
#### 2.2.2 Oversampling Methods  
For datasets with less information in particular classes to be balanced, artificial samples must be generated to increase the number of rare instances [30]. To create an equal class distribution by resizing the classes in this study, the following types of oversampling methods were used: methods based on the minority class, namely SMOTE and ADASYN, and methods based on the decision boundary, namely SVM and Borderline. The SMOTE technique generates new instances of the minority class, resulting in improved performance in minority-class prediction. There are many varieties of SMOTE preprocessing techniques, namely traditional SMOTE, Borderline-SMOTE1, Borderline-SMOTE2, SMOTE-NC, and SVM-SMOTE, which were used in a hospital mortality prediction study of 126 patients with traumatic injuries. The data were extracted from the patients' medical records. The researchers focused on the trauma patients' status (alive/dead) as an outcome and six risk factors (age, sex, type of trauma, location of injuries, Glasgow coma scale, and white blood cells). The results showed that among all SMOTE-based ML methods, RF and ANN with SMOTE and XGBoost with SMOTE-NC achieved the highest values for all evaluation metrics [30]. [PERSON] [31] proposed a novel Data Augmented SMOTE Multi-Class Classifier (DASMcC) to predict Cardiovascular Diseases (CVDs). To ensure that the classifier's performance was not biased, SMOTE was combined with a 10-fold cross-validation technique, while XGBoost performed the best overall. SVM is an ML algorithm for binary classification, according to which the input vector performs a non-linear mapping process to create linearly separable data in a higher-dimensional feature space. Removing correlated features and simplifying the model-training process with PCA whitening results in data with unit variance along each dimension in the pretraining process of an imbalanced dataset [13]. [PERSON] [32] presented a novel ensemble model based on SVM's hyperplane to calculate the optional boundaries between the signed distances integrated with bLR, resulting in higher accuracy compared to SVM kernel selection using datasets related to the ionosphere, High Time-Resolution Universe Survey (HTRU2) pulsar candidate, diabetes, and liver disorders. Borderline oversampling is an advanced variation of SMOTE that generates samples near the decision boundaries of classes. [PERSON] and [PERSON] [5] proposed a novel method called minority oversampling near the borderline with a generative adversarial network (OBGAN) that uses Borderline with a generative adversarial network to focus on avoiding the mode-collapse problem on small datasets. Among 21 relatively small imbalanced datasets, examples of majority/minority outcomes include the following: liver cancer (have/not), with 10 features; breast cancer (benign/malignant), with 9 features; Pima Indian diabetes (diabetes/not), with 10 features; blood transfusion (not/donate), with 4 features (from the UCI ML Repository, Kaggle, and DataHub); and 6 benchmark methods (SMOTE, Borderline-SMOTE, ADASYN, k-means SMOTE (kmSMOTE), conditional GAN, and Generative Adversarial Minority Oversampling (GAMO). The experiments show OBGAN is competitive with the SMOTE-based methods and achieves stable performance for multiclass problems with various majority-minority ratios. The ADASYN technique adapts the generation process based on the density of the distribution of minority-class instances. [PERSON] [33] proposed a DAD-net system to detect [PERSON]'s disease from images for which ADASYN was able to generate new samples to balance the number of instances in every category. This section underlines the fact that databases of common health problems like cancer, Parkinson's disease, and depression are widely used in the medical field for predictive purposes. However, some datasets are affected by common data issues such as missing values, outliers, and imbalanced data.