CERV‐Score: A Hybrid Machine Learning Framework for Cervical Cancer Risk Prediction Using Integrated Clinical and Genomic Data

Cervical cancer remains a major global health burden, particularly in underserved populations where late diagnoses contribute to high mortality rates. Accurate, early risk prediction is essential for improving outcomes and guiding preventive care. In this study, we introduce CERV‐Score, a hybrid machine learning framework that advances prior approaches by combining structured clinical risk factors with recurrence‐based genomic markers to generate continuous, probabilistic risk scores rather than traditional binary classifications. This enables nuanced patient stratification into low, moderate, and high‐risk categories, providing clinicians with more actionable insights. Unlike previous models, CERV‐Score integrates genomic recurrence analysis identifying genes consistently expressed across multiple RNA‐seq samples to improve biological relevance and robustness. Additionally, we developed an interactive clinical‐genomic decision support tool that delivers real‐time, percentage‐based risk predictions and includes a gene lookup function, bridging clinical practice and molecular exploration in a single platform. The hybrid CERV‐Score model achieved high predictive performance (accuracy = 94.1 % , F1 − score = 0.91, AUC = 0.94). Bootstrap resampling (1000 iterations) applied to the test predictions produced a 95% confidence interval for accuracy of 92.8%–95.4%, confirming the stability and robustness of the model′s performance. These results highlight the contribution of probabilistic scoring, recurrence‐driven genomic integration, and interactive visualization to enhance both accuracy and usability. By combining methodological innovation with practical clinical utility, CERV‐Score represents a meaningful step beyond existing hybrid models, laying the groundwork for more interpretable, personalized, and deployable cervical cancer risk prediction systems.