NEDL-GCP: A nested ensemble deep learning model for Gynecological cancer risk prediction

Abstract

Gynecological cancer remains a critical global health concern, where early detection significantly improves patient outcomes. Despite advances in deep learning for medical diagnostics, existing models often struggle with feature redundancy, lack of generalizability, and suboptimal integration of diverse feature representations, limiting their effectiveness in clinical applications. In this study, we present NEDL-GCP, a Nested Ensemble Deep Learning model for Gynecological Cancer Risk Prediction, which uses a hierarchical ensemble framework to improve the accuracy of the classification. NEDL-GCP integrates CNNs, RNNs, and SVMs as base learners, extracting diverse feature representations, while a meta-classifier combining J48 and Stochastic Gradient Descent (SGD) refines predictions. Evaluated on the Herlev and SIPaKMeD Pap Smear datasets, NEDL-GCP achieved state-of-the-art accuracy scores of 99.1% and 98.5%, outperforming existing methods. These results demonstrate the robustness and reliability of the model, making it a valuable tool for the early detection of cervical cancer. By enhancing diagnostic accuracy and optimizing clinical workflows, NEDL-GCP supports timely decision-making, ultimately improving patient care.