ArticleFrontiers in oncology2023
A comparison of machine learning models and Cox proportional hazards models regarding their ability to predict the risk of gastrointestinal cancer based on metabolic syndrome and its components.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 17 citations in OpenAlex.
- Quantifying the Predictive Power of Social Determinants of Health in Cardiovascular Disease and Type 2 Diabetes Progression Using XGBoost: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- Advancing Gastrointestinal Cancer Risk Prediction With Patient-Centered Machine Learning: Machine Learning Modeling Study.JMIR medical informatics · 2026Article
- Machine Learning-Based Survival Prediction Models for Young Patients With Gastric Cancer: Model Development and Validation Study.JMIR cancer · 2026Article
- Development of a machine learning-based prognostic prediction model and a web-based tool for pediatric hepatoblastoma: a Surveillance, Epidemiology, and End Results (SEER) database study.Pediatric surgery international · 2026Article
- Prediction of First and Multiple Antiretroviral Therapy Interruptions in People Living With HIV: Comparative Survival Analysis Using Cox and Explainable Machine Learning Models.JMIR medical informatics · 2026Article
- Machine Learning Models for Disease-Free Survival Analysis after Liver Resection for Hepatocellular Carcinoma: A Multicentric French Collaborative Study.Liver cancer · 2026Article
- Development of predictive models for the prognosis of triple-negative breast cancer using multiple transcriptomic analyses.PloS one · 2026Article
- Predicting targeted therapy resistance in non-small cell lung cancer using multimodal machine learning.Journal of thoracic disease · 2025Article
- Comparison of machine learning and Cox regression models for prognostic analysis in hepatocellular carcinoma patients with distant metastasis.Surgery open science · 2025Article
- The association of diet-dependent acid load with gastrointestinal cancer risk in the Cancer Screenee Cohort in Korea.European journal of clinical nutrition · 2025Article
- Article
- Machine learning algorithms that predict the risk of prostate cancer based on metabolic syndrome and sociodemographic characteristics: a prospective cohort study.BMC public health · 2024Article
- Predicting Prognosis of Early-Stage Mycosis Fungoides with Utilization of Machine Learning.Life (Basel, Switzerland) · 2024Article
- Factors affecting the survival of prediabetic patients: comparison of Cox proportional hazards model and random survival forest method.BMC medical informatics and decision making · 2024Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Little is known about applying machine learning (ML) techniques to identify the important variables contributing to the occurrence of gastrointestinal (GI) cancer in epidemiological studies. We aimed to compare different ML models to a Cox proportional hazards (CPH) model regarding their ability to predict the risk of GI cancer based on metabolic syndrome (MetS) and its components. Methods: A total of 41,837 participants were included in a prospective cohort study. Incident cancer cases were identified by following up with participants until December 2019. We used CPH, random survival forest (RSF), survival trees (ST), gradient boosting (GB), survival support vector machine (SSVM), and extra survival trees (EST) models to explore the impact of MetS on GI cancer prediction. We used the C-index and integrated Brier score (IBS) to compare the models. Results: In all, 540 incident GI cancer cases were identified. The GB and SSVM models exhibited comparable performance to the CPH model concerning the C-index (0.725). We also recorded a similar IBS for all models (0.017). Fasting glucose and waist circumference were considered important predictors. Conclusions: Our study found comparably good performance concerning the C-index for the ML models and CPH model. This finding suggests that ML models may be considered another method for survival analysis when the CPH model's conditions are not satisfied.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.