## 1 Introduction  
Cervical cancer is the fourth most commonly diagnosed cancer in women around the world [1]. According to [2], the most important risk factors and potential predictors of -cervical cancer are smoking, infection with sexually transmitted diseases (HIV, syphilis, etc.), and the use of hormonal contraceptives. These risk factors underline the importance of using demographic and medical information as indicators for cervical cancer risk prediction. Analyzing these factors can help us to understand how lifestyle choices and medical conditions correlate with the development of cervical cancer.  
Prediction, particularly classification, and, in general, decision making based on imbalanced datasets can lead to diverse difficulties in data mining [3; 4]. The studies reported in [5; 6; 7] presented recent state-of-the-art methods for healthcare data-balancing issues, with promising results.  
In this work, the main focus is on balancing issues in cervical cancer prediction based on messy, imbalanced data. One of the aims of this work was to study and compare a broad range of frequently applied data-balancing methods, combined with ML classification methods, and to evaluate the performance of these methods before and after balancing on the cervical cancer dataset. In the selection of the ML classification method, several key factors were taken into consideration. K-nearest neighbors makes local decisions, not using global patterns [8], and computationally efficient logistic regression is frequently used in medical research [9], while random forest can handle non-linear relationships well with ensemble learning [10]. These algorithms are widely used in healthcare research when dealing with imbalanced data. However, if scientists do not treat this problem appropriately, could lead to wrong research design.  
This study investigated the following eight undersampling methods: Condensed Nearest Neighbor (CNN), Tomek Links (TLs), Edited Nearest Neighbor (ENN), Repeated Edited Nearest Neighbors (RENN), All K-Nearest Neighbors (All-KNN), NearMiss (NM), Neighborhood Cleaning Rule (NCR), and Instance Hardness Threshold (IHT). The following four oversampling methods were also studied: the Synthetic Minority Oversampling Technique (SMOTE); the Adaptive Synthetic Sampling Approach for Imbalanced Learning (ADASYN); methods based on the decision boundary, such as Support Vector Machine (SVM) and Borderline; and ML algorithms KNN, LR, and RF. By comparing these methods, we provide knowledge to improve classification accuracy while offering both experimental results and statistical characterization of the data.  
This research subject is also motivated by the fact that in many studies include experimental evaluations of algorithms without characterization of the data to which they are applied. Such characterizations could provide a clear indication for other researchers regarding the applicability of algorithms to their specific data. Specifically, if the data have the respective characteristics, performance evaluation results similar to those reported in the research can be expected.  
This research is presented under six main sections. Section 2 presents a state-of-the-art bibliographic study on balancing techniques with state-of-the-art classification algorithms in healthcare prediction. Section 3 outlines the steps of data preprocessing and the proposed classification methodology. Based on this, Section 4 presents the results achieved when applying the eleven studied sampling methods with the three studied ML methods, and Section 5 highlights the results in comparison with similar works. Finally, Section 6 summarizes the main ideas and proposes future work in the medical field.