ArticleScientific reports2024
Comparative study of machine learning and statistical survival models for enhancing cervical cancer prognosis and risk factor assessment using SEER data.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
- Development and multi-center validation of a machine learning‑based prediction model for mortality in tumor-related sepsis.BMC infectious diseases · 2026Article
- Enhanced cervical cancer diagnosis using a novel Bayesian fusion ensemble method with explainable AI.Scientific reports · 2026Article
- Predicting adverse outcomes in dilated cardiomyopathy using 3D echocardiography: penalised Cox regression versus machine learning.BMC cardiovascular disorders · 2026Article
- Predictive modeling for cervical cancer: existing AI approaches and the emerging role of vaginal microbiome.Frontiers in network physiology · 2026Article
- Development and validation of an interpretable machine learning-based predictive model for breast cancer bone metastasis.Frontiers in oncology · 2026Article
- Machine learning and cancer survival: analysis of the Registro Hospitalar de Câncer do Estado de São Paulo.Revista de saude publica · 2026Article
- A novel tumor microenvironment-related gene signature for prognostic prediction of intrahepatic cholangiocarcinoma.Scientific reports · 2025Article
- Feature Selection and Model Optimization for Survival Prediction in Patients with Angina Pectoris.Journal of clinical medicine · 2025Article
- Assessing the accuracy of survival machine learning and traditional statistical models for Alzheimer's disease prediction over time: a study on the ADNI cohort.BMC medical research methodology · 2025Article
- Machine Learning-Based Prognostic Modelling Using MRI Radiomic Data in Cervical Cancer Treated with Definitive Chemoradiotherapy and Brachytherapy.Current oncology (Toronto, Ont.) · 2025Article
- Automated feature learning and survival prognostication in grade 4 glioma using supervised machine learning models.Journal of neuro-oncology · 2025Article
- Application of Machine Learning for Predicting Progression-Free and Overall Survival in Patients With Renal Cell Carcinoma.Clinical and translational science · 2025Article
- Global determinants of gynecologic cancer incidence and mortality: A cluster-based analysis with predictive insights.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2025Article
- PTK6 mediated immune signatures revealed by single cell transcriptomic and multi omics big data analysis in cervical cancer.Discover oncology · 2025Article
- Comparative study of five-year cervical cancer cause-specific survival prediction models based on SEER data.Scientific reports · 2025Article
- Recent advances in applications of machine learning in cervical cancer research: a focus on prediction models.Obstetrics & gynecology science · 2025Article
- Explainable transformer-based deep survival analysis in childhood acute lymphoblastic leukemia.Computers in biology and medicine · 2025Article
- Cervical cancer screening uptake and its associated factor in Sub-Sharan Africa: a machine learning approach.BMC medical informatics and decision making · 2025Article
- On a Bayesian multivariate survival tree approach based on three frailty models.Scientific reports · 2025Article
- Development and validation of hybrid machine learning approach for predicting survival in patients with cervical cancer: a SEER-based population study.Frontiers in oncology · 2025Article
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Abstract
Cervical cancer is a common malignant tumor of the female reproductive system and the leading cause of death among women worldwide. The survival prediction method can be used to effectively analyze the time to event, which is essential in any clinical study. This study aims to bridge the gap between traditional statistical methods and machine learning in survival analysis by revealing which techniques are most effective in predicting survival, with a particular emphasis on improving prediction accuracy and identifying key risk factors for cervical cancer. Women with cervical cancer diagnosed between 2013 and 2015 were included in our study using data from the Surveillance, Epidemiology, and End Results (SEER) database. Using this dataset, the study assesses the performance of Weibull, Cox proportional hazards models, and Random Survival Forests in terms of predictive accuracy and risk factor identification. The findings reveal that machine learning models, particularly Random Survival Forests (RSF), outperform traditional statistical methods in both predictive accuracy and the discernment of crucial prognostic factors, underscoring the advantages of machine learning in handling complex survival data. However, for a survival dataset with a small number of predictors, statistical models should be used first. The study finds that RSF models enhance survival analysis with more accurate predictions and insights into survival risk factors but highlights the need for larger datasets and further research on model interpretability and clinical applicability.
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