Development and validation of hybrid machine learning approach for predicting survival in patients with cervical cancer: a SEER-based population study

Development and validation of hybrid machine learning approach for predicting survival in patients with cervical cancer: a SEER-based population study

BackgroundAccurate survival prediction in cervical cancer is crucial for personalized therapy, particularly in high-risk groups where early intervention might enhance results. The study aims to create a hybrid survival model that integrates Cox Proportional Hazards (CoxPH) with Elastic Net regulariz...

Full description

Saved in:
Bibliographic Details
Main Authors: Anjana Eledath Kolasseri, Venkataramana B.
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-06-01
Series:Frontiers in Oncology
Subjects:
cervical cancer
SEER database
machine learning
survival models
hybrid models
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2025.1605378/full
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:BackgroundAccurate survival prediction in cervical cancer is crucial for personalized therapy, particularly in high-risk groups where early intervention might enhance results. The study aims to create a hybrid survival model that integrates Cox Proportional Hazards (CoxPH) with Elastic Net regularization and Random Survival Forest (RSF) to improve prediction accuracy and interpretability.MethodsData from the SEER database (2013–2015) were pre-processed through normalization and encoding. RSF recorded non-linear interactions between covariates, while the CoxPH Elastic Net Regularization model provided linear interpretability and identified key variables. Model parameters were optimized using cross-validation, and final performance was assessed on an independent test set using metrics including C-index, Integrated Brier Score (IBS), AUC-ROC, and calibration plots.ResultsThe hybrid model outperformed the individual models with an Integrated Brier Score (IBS) of 0.13 and a concordance index (C-index) of 0.82. With an AUC-ROC of 0.84, the model provided robust calibration and classification performance on the independent test set, effectively separating between individuals at high and low risk.ConclusionThe hybrid model provides a promising tool for personalized risk stratification in cervical cancer based on survival probability. Further testing in varied clinical categories is recommended to confirm its efficiency in precision oncology.
ISSN:2234-943X

Similar Items