Class imbalance can significantly impact the performance of learning algorithms, often leading to prediction bias toward the majority class. This challenge is particularly critical in healthcare-related domains, as medical datasets are often imbalanced, hindering the accurate prediction of the minority class, which is commonly the class of interest. As such, this work introduces a novel resampling algorithm, designated Genetic Beta Oversampling, which integrates user-defined preferences into the synthetic data generation process, allowing fine control over the model’s inclination towards false negatives or false positives. These user preferences are encoded in the form of a parameter, \(\beta \) , which dictates the trade-off between recall and precision that the method should seek to achieve. This flexibility is particularly relevant in clinical settings, where prioritizing recall can enhance patient care by reducing missed diagnoses. We validate the approach using the EMPA dataset for the classification of pain induced by a cold stimulus, where prioritizing recall is essential to minimize missed pain detections. Experimental results demonstrate that our method outperforms SMOTE, SMOTE-IPF, and four cost-sensitive classifiers in terms of F \(\beta \) -score across diverse \(\beta \) settings. These findings underscore the method’s adaptability in recall-sensitive applications, such as pain assessment and clinical decision-making.