# Highly Imbalanced Classification of Gout Using Data Resampling and Ensemble Method  
[PERSON]  
1 School of Biomedical Engineering, Division of Life Science and Medicine, University of Science and Technology of China, Hefei 230026, China; [EMAIL_ADDRESS]. which can lead to biased predictions towards the majority class [6]. The class imbalance problem (CLP) is the non-uniform distribution of classes in a dataset. The term'majority class' refers to the class with the highest number of instances, while the term'minority class' refers to the class with the lowest number of instances [7]. A classifier trained with imbalanced data tends to be biased towards the majority class and may overlook the more important minority class. Models often exclude the minority class in order to achieve higher accuracy, which can lead to biased results. Addressing data imbalance in gout can improve the model's ability to learn the features from the minority classes, reducing the risk of misdiagnosis during gout diagnosis. In medical diagnostics, the SMOTE algorithm, which creates synthetic examples by interpolating between minority class instances and their k-nearest neighbors, is the primary method for addressing class imbalance [8]. Nevertheless, SMOTE has some weaknesses. It depends on the parameter k and the quality of the generated samples; it also has an over-density of synthetic samples. On the other hand, medical diagnostics often employ separate classifiers that fail to fully extract the dataset's features, which reduces the classification's effectiveness [9]. This study addresses the lack of research for accurately staging gout beyond a simple diagnosis and aims to address the imbalance in gout data. To address the imbalanced data problem, this study proposes a new hybrid sampling strategy based on an extended natural neighbor. And, ensemble learning is then combined to alleviate the problem of data imbalance. After acquiring and preprocessing data from hospital medical records, ENaNSMOTE-Tomek link is proposed as a solution to the imbalanced data problem caused by the gout dataset. ENaNSMOTE-Tomek link uses extended natural neighbors with SMOTE to generate new samples for the minority class and removes noise with Tomek link, achieving data balance. The bagging ensemble strategy is used to improve the recognition accuracy of both the majority and minority classes by addressing the uneven distribution of the data. Because the study utilized a dataset with an imbalanced distribution of classes and a relatively high number of features, correlation analysis and random forest are used to select features that improve the accuracy of the proposed models. Feature selection aims to identify the features that significantly impact the final prediction results, and random forest can calculate feature importance and reduce model calculation costs. Also, this study uses six classifiers--support vector machines (SVMs), decision trees (DTs), k-nearest neighbors (KNN), gradient boosting (GB), multilayer perceptron (MLP), and extreme gradient boosting (XGB)--and selects the optimal classifier through a proposed resampling method and ensemble learning for the diagnosis of gout. This study employs commonly used classification performance metrics, including accuracy, precision, recall, and F1 score. Additionally, AUC, a performance metric that captures imbalanced classification, is also utilized. All experimental results are based on physical examination and clinical laboratory indicators of real gout patients. Figure 1 illustrates the method and process of this algorithm. The key contributions of this study are as follows:  
(1) A predictive model is proposed for accurately classifying different periods of gout. Experimental results demonstrate that it outperforms the same type of disease diagnosis approach for diseases such as heart disease and diabetes. (2) The ENaNSMOTE-Tomek link algorithm is proposed to address the issue of imbalanced data.