## 2 Bibliographic Study
### Sampling Methods  
#### 2.2.1 Undersampling Methods  
To handle class imbalance, the following nearest neighbor-based methods were considered appropriate and were chosen for this study: CNN, TL, ENN, RENN, All-KNN, NM, NCR, and the iterative IHT method. For an accurate analysis, undersampling reduces the number of abundant observations to create two equally sized classes [13]. The CNN undersampling technique reduces the size of the majority class by keeping the most informative instances to maintain the decision boundary. The work reported in [23] used a one-pass variation of CNN called Instance-Based Learning 2 (IB2) and proposed a novel variation algorithm based on it. The researchers tested it on nine datasets, including variables with different types of data, some of which were related to the medical field (such as yeast, congenital heart disease, and water quality), achieving decreased computational cost for the multilabel classification process. TL identifies pairs of observations from different classes that are the nearest neighbors to each other, but when combined with the SMOTE method, it can balance the training dataset and eliminate components that are on the wrong side of the decision. On a lung cancer dataset, this hybrid TL approach with balanced RF resulted in the highest mean AUC compared to other hybrid methods, including the baseline and SMOTE-Edited Nearest Neighbors (SMOTE-ENN) [24]. All-KNN removes observations from the majority class that have at least one nearest neighbor in the minority class to increase the separability of the classes. In a case study of the Parkinson's Disease Tremor Severity Classification (a clinical dataset collected from a wearable sensor in laboratory and home environments), All-KNN with Artificial NN based on multi-layer perceptron (ANN-MLP) metrics such as accuracy, precision, and sensitivity improved, but the Index of Balanced Accuracy (IBA) and geometric mean stayed low [25]. RENN removes misclassified observations from the majority class until a desired class balance is achieved. The study reported in [11] used four imbalanced health datasets concerning diabetes, anemia, lung cancer, and obesity, featuring high-dimensional data with non-linear relationships. The researchers underlined that RENN with LR is more effective in handling the lack of symmetry in dataset distribution. ENN is a cleaning technique but eliminates misclassified observations in one step without any iterative approach. A possible application of the ENN undersampling technique is in Medicare fraud detection. In [26], ENN was combined with SMOTE methods on the Medicare Part B dataset. The results showed that this hybrid approach effectively balanced synthetic observations while eliminating noisy data, and Decision Tree (DT) outperformed the following ML classifiers: Extreme Gradient Boosting (XGBoost), Adaptive Boosting (Adaboost), Light Gradient Boosting Machine (LGBM), LR, and RF. The NCR technique removes misclassified majority observations and improves decision boundaries. In [27], a novel clustering approach was presented that improved the quality of classification. The authors used the K-Means algorithms to cluster the data; then, clusters of only the majority class at a specified distance from the center were removed. For the Yeast5 protein-protein interaction network dataset, the NCR method provided the best results in terms of the Matthews Correlation Coefficient (MCC) and Cohen's Kappa statistic (Kappa) metrics. The NM technique selectively removes majority-class instances based on the distances to minority-class instances (NM-1 removes the closest element, and NM-2 removes the farthest element from the minority class). Combining NM with PCA, [PERSON] and [PERSON] [28] presented a novel approach to address the class imbalance problem in healthcare data.