A Hybrid Statistical Deep Learning Framework for Breast Cancer Survival Prediction Using Covariate Transformation and Simulated Gene Expression Data

Abstract Accurate prediction of breast cancer survival is crucial for precision oncology and individualized treatment planning. Standard survival models, however, typically fail to capture nonlinear clinical relationships, latent biological heterogeneity, and complex interactions among prognostic factors. To address these limitations, this study introduces a Hybrid Machine Learning Framework that combines adaptive covariate transformation, simulated gene expression generation, cross-modal attention fusion, graph-based patient learning, dynamic multi-expert survival modeling, and Bayesian uncertainty estimation. The framework translates clinical variables into clinically meaningful latent representations of prognosis and creates biologically plausible molecular features to boost prognosis prediction. Experimental results show that the proposed model achieves better C-index, AUC, Precision, Recall, and F1-score (0.927, 0.944, 0.931, 0.924, and 0.927, respectively) and a minimum Integrated Brier Score (0.081). Survival stratification analysis clearly differentiates among the low-, intermediate-, and high-risk patient groups, and ablation analysis results support the contribution of each component of the framework. The results suggest that combining transformed clinical data with synthetic genomic data yields a powerful, interpretable, and uncertainty-aware prediction tool for breast cancer survival, with promising potential for precision oncology applications.