Survival Period Prediction in Cervical Cancer Patients Using the Selective Stacking Technique

This study aims to increase the effectiveness of cervical cancer treatment by developing a survival prediction model using an innovative ensemble machine learning approach, namely the selective stacking technique.

Patient data obtained from the Faculty of Medicine, Chiang Mai University, Thailand, were utilized to validate the real-world applicability of the proposed approach. The selective stacking model employed a two-stage machine learning framework in which outputs from base machine learning models were systematically combined through meta-level learning. Importantly, the performance of the proposed model was compared with that reported in previous studies that relied on individual machine learning algorithms as baselines. To provide deeper insight into the predictive mechanisms of the model, local interpretable model-agnostic explanations were applied to assess feature importance and identify the most influential factors contributing to model predictions.

The classification model developed using the selective stacking technique demonstrated a marked improvement in prediction accuracy, achieving an accuracy of 91.41%. The regression model also showed robust performance, with a root mean square error of 18.92 and an r value of 0.669. Feature importance analysis indicated that side effect status involving surrounding organs emerged as the most influential factor in survival prediction.

The selective stacking model exhibited superior predictive performance compared with the base models, suggesting that this approach offers a promising strategy for cervical cancer survival prediction and may support the development of more personalized treatment planning.