ArticleCancers2024
A Novel Approach for Predicting the Survival of Colorectal Cancer Patients Using Machine Learning Techniques and Advanced Parameter Optimization Methods.
Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Established machine learning matches tabular foundation models in clinical predictions.BMC medical informatics and decision making · 2026Article
- Prognostic Power of Ensemble Learning in Colorectal Cancer with Peritoneal Metastasis: A Multi-Institutional Analysis.Bioengineering (Basel, Switzerland) · 2026Article
- Short-term mortality prediction in children with gastrointestinal congenital anomalies using a random forest classifier.Pediatric research · 2026Article
- Machine learning application in colon cancer treatment outcome prediction.Scientific reports · 2026Article
- Study on ensemble model with weight allocation based on improved dung beetle optimization algorithm for screening colorectal cancer using laboratory test indicators.Journal of gastrointestinal oncology · 2025Article
- Capsule Neural Networks with Bayesian Optimization for Pediatric Pneumonia Detection from Chest X-Ray Images.Journal of clinical medicine · 2025Article
- Machine learning to evaluate the effects of non-clinical social determinant features in predicting colorectal Cancer mortality in a medically underserved Appalachian population.Scientific reports · 2025Article
- Optimizing prediction of metastasis among colorectal cancer patients using machine learning technology.BMC gastroenterology · 2025Article
- Towards precision oncology: a multi-level cancer classification system integrating liquid biopsy and machine learning.BioData mining · 2025Article
- Gamma-Glutamyl Transferase Plus Carcinoembryonic Antigen Ratio Index: A Promising Biomarker Associated with Treatment Response to Neoadjuvant Chemotherapy for Patients with Colorectal Cancer Liver Metastases.Current oncology (Toronto, Ont.) · 2025Article
- Article
- Machine learning-based identification of proteomic markers in colorectal cancer using UK Biobank data.Frontiers in oncology · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundColorectal cancer is one of the most prevalent forms of cancer and is associated with a high mortality rate. Additionally, an increasing number of adults under 50 are being diagnosed with the disease. This underscores the importance of leveraging modern technologies, such as artificial intelligence, for early diagnosis and treatment support.
methodsEight classifiers were utilized in this research: Random Forest, XGBoost, CatBoost, LightGBM, Gradient Boosting, Extra Trees, the k-nearest neighbor algorithm (KNN), and decision trees. These algorithms were optimized using the frameworks Optuna, RayTune, and HyperOpt. This study was conducted on a public dataset from Brazil, containing information on tens of thousands of patients.
resultsThe models developed in this study demonstrated high classification accuracy in predicting one-, three-, and five-year survival, as well as overall mortality and cancer-specific mortality. The CatBoost, LightGBM, Gradient Boosting, and Random Forest classifiers delivered the best performance, achieving an accuracy of approximately 80% across all the evaluated tasks.
conclusionsThis research enabled the development of effective classification models that can be applied in clinical practice.
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Registered trials
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.