Diagnosis of Cervical Cancer Based on a Hybrid Strategy with CTGAN

Cervical cancer remains a significant global public health challenge, particularly in low- and middle-income countries where invasive diagnostic methods are underutilized due to limited medical resources. Machine learning has provided a new pathway to address this challenge, but existing machine learning prediction methods face three major challenges: feature redundancy, class imbalance, and sample scarcity. To address these issues, this study proposes a hybrid data processing strategy with Conditional Tabular Generative Adversarial Networks (CTGAN) and machine learning to construct a more accurate and efficient auxiliary diagnostic model for cervical cancer. The hybrid strategy first employs the Minimal Redundancy Maximal Relevance (mRMR) algorithm and XGBoost-based Recursive Feature Elimination (RFE) for secondary feature screening. Subsequently, the SMOTE-ENN combination sampling method is applied to handle extreme class imbalance, and CTGAN is utilized to augment the dataset, thereby mitigating data scarcity. Experimental validation on the Risk Factors of Cervical Cancer (RFCC) dataset from a Venezuelan hospital demonstrates that, after processing with the proposed hybrid strategy, the Logistic Regression (LR) model achieves the best overall prediction results, with accuracy, precision, recall, and F1-score reaching 99.00%, 99.28%, 98.77%, and 99.02%, respectively, outperforming existing methods.