## 6 Conclusions  
Based on the performed research, we conclude that datasets in a class with significantly fewer instances lead to overfit classifiers that favor the majority class. Based on this fact, the observations in the minority class are hardly recognizable or incorrectly identified. This issue is present in the medical field because, in many cases, the number of records corresponding to healthy patients (majority class) is much larger than the number of records for patients diagnosed with a specified disease (minority class). This leads to significant information related to minority classes corresponding to real-word medical problems being ignored, information that requires careful examination for prediction. In our case, none of the studied oversampling methods (SMOTE, SVM SMOTE, Borderline SMOTE, or ADASYN), in combination with ML methods (KNN, LR, or RF) provided the best results in the case of any of the investigated evaluation metrics. KNN obtained higher metric values with the Borderline SMOTE method and RF than with the ADASYN method. It is also notable that in experiments, KNN3 performed better than KNN2. KNN3 achieved a balanced accuracy of 73.78%, while KNN2 scored 63.89%. KNN3 outperformed KNN2 in minority-class performance, scoring 55.72% compared to KNN2's 33.93%. RF achieved good results with all oversampling methods, even before applying sampling techniques. This fact could lead to the use of compound classifiers in the future, although it is also important to pay attention to relevant attributes and eliminate irrelevant ones with the help of RF [22]. This paper presents a comprehensive research study of a large number of sampling methods used for data balancing with both over- and undersampling methods and discusses different modified versions. This study also serves as a practical guide for stakeholders because when using data with these characteristics, similar results can be expected when applying the investigated algorithms and balancing methods. In future work, a set of hybrid classifiers (combining different classifiers and applying ensemble methods or feature-level hybridization with similar feature selection methods toPCA), in combination with under- and oversampling methods, could be be applied to the data used in this study. Conceptualization, Z.S., M.M.M. and L.B.I. ; methodology, Z.S. and M.M.M. ; software, M.M.M. and Z.S. ; validation, M.M.M. and Z.S. ; formal analysis, L.B.I. and M.M.M. ; investigation, Z.S. and M.M.M. ; resources, M.M.M. ; data curation, M.M.M. ; writing--original draft preparation, Z.S. ; writing--review and editing, Z.S., M.M.M. and L.B.I. ; visualization, M.M.M. and Z.S. ; supervision, L.B.I. ; project administration, L.B.I. ; funding acquisition, Z.S. and L.B.I. All authors have read and agreed to the published version of the manuscript. The article processing charge (APC) was funded by the Institution Organizing University Doctoral Studies (I.O.S.U.D. ), the Doctoral School of Letters, Humanities, and Applied Sciences, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 54014 Targu Mures, Romania. Not applicable. The datasets used in this article are publicly available for download at [[https://archive.ics.uci.edu/dataset/383/cervical+cancer+risk+factors](https://archive.ics.uci.edu/dataset/383/cervical+cancer+risk+factors)]([https://archive.ics.uci.edu/dataset/383/cervical+cancer+risk+factors](https://archive.ics.uci.edu/dataset/383/cervical+cancer+risk+factors)) (last accessed on 16 August 2024). We would like to thank the Research Center on Artificial Intelligence, Data Science, and Smart Engineering (ARTEMIS) and COST Action CA22137--Randomised Optimisation Algorithms Research Network (ROAR-NET) for their support. The authors declare no conflicts of interest.