# Highly Imbalanced Classification of Gout Using Data Resampling and Ensemble Method
## 2 Literature Review  
The diagnosis of gout patients is primarily based on pathology and pharmacology. For instance, [PERSON] et al. classified patients with gout and clinical hyperuricemia based on target serum urate levels [10]. [PERSON] et al. utilized oxylipin biomarkers to differentiate between gout and hyperuricemia [11]. [PERSON] et al. used potential biomarkers of metabolites to distinguish between gout and hyperuricemia [12]. Also, some progress has been made in predicting gout using traditional machine learning. [PERSON] et al. employed machine learning and natural language processing techniques to automatically detect gout attacks from electronic clinical records [13]. [PERSON] et al. introduced neighborhood rough sets into multivariate variational mode decomposition and used them to construct a method for classifying potential gout patients [14]. [PERSON] et al. applied deep reinforcement learning to solve the gout staging task [15]. Research has been conducted in the literature to develop disease diagnostics based on machine learning models in order to create more accurate prediction models. [PERSON] et al. used a random forest algorithm, while [PERSON] et al. employed the k-nearest neighbors method to predict the factors that contribute to perceived stress [16; 17]. [PERSON] et al. utilized support vector machines to predict students' happiness [18]. [PERSON] et al. and [PERSON] et al. both used machine learning algorithms to predict the onset and early-stage risk of diabetes, respectively. [PERSON] et al. employed a decision tree, while [PERSON] et al. used a random forest [19; 20]. [PERSON] et al. proposed a KNN + SOM + PCA + Fuzzy support vector machine (SVM) model for diagnosing heart disease [21]. [PERSON] et al. used decision trees to predict heart disease by utilizing clinical records [22]. [PERSON] et al. utilized a gradient boosting classifier to diagnose human heart disease [23]. Resolving imbalanced data can improve predictions and reduce errors in medical diagnosis. For data balancing in cardiovascular disease, RandomOverSampler has been used [24]. In order to improve the survival rate of heart failure patients, the extra tree classifier (ETC) was proposed; it uses SMOTE to balance the data [25]. Also, the authors  
Figure 1: Algorithm flow diagramused SMOTE to classify diabetes and reliable stress levels [26; 27]. [PERSON] et al. proposed using extreme gradient boosting with SMOTE-ENN to solve the cardiovascular prediction problem [28]. The use of ensemble methods for classification has gained momentum in recent years. Ensemble techniques combine the predictions of multiple base classifiers to produce a final result, resulting in improved accuracy. They are objective and avoid subjective evaluations, as demonstrated from the following articles. For example, [PERSON] employed different machine learning methods with majority votes to predict credit card fraud transactions [29]. In the field of disease diagnosis, [PERSON] utilized DNN, IF, and LR with ensemble learning to evaluate stroke records [30]. Meanwhile, [PERSON] employed random forest, extra tree (ET), AdaBoost, and MLP (multilayer perceptron) with ensemble learning, along with various feature selection methods, to classify polycystic ovary syndrome [31]. Similarly, [PERSON] used AdaBoost ensemble learning to classify neuromuscular disorders, while [PERSON] developed machine learning models using ensemble methods to identify patients with VIPN-free survival [32; 33]. [PERSON] employed an ensemble voting method to combine random forest, extreme gradient boosting, and gradient boosting to enhance the prediction of heart disease [9]. Previous studies have shown that disease diagnosis models perform well. However, there are still several shortcomings. Classification on imbalanced datasets can result in biased outcomes, as most standard classification algorithms favor the majority class, leading to poor prediction accuracy for the minority class.