ArticleBMC cancer2023
Machine learning-based colorectal cancer prediction using global dietary data.
Article in BMC cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Machine learning integration of tissue-specific metagenomic signatures for colorectal cancer diagnosis.Journal of applied genetics · 2026Article
- Dual-attention bidirectional LSTM with feature genomic analysis improves prognostic survival prediction in colorectal cancer patients.Journal of the Egyptian National Cancer Institute · 2026Article
- Machine Learning Unveils Dietary Antioxidants as Influential Factors for Diabetes-Cancer Comorbidity: Insights From National Health and Nutrition Examination Survey.Food science & nutrition · 2026Article
- Artificial Intelligence in Population-Level Gastroenterology and Hepatology: A Comprehensive Review of Public Health Applications and Quantitative Impact.Digestive diseases and sciences · 2026Review
- Colorectal Lesion diagnosis using transformer and deep learning with multiscale feature interface.PloS one · 2026Article
- Exploring Machine Learning Approaches for Decision Support in Neoadjuvant Therapy of Locally Advanced Rectal Cancer.Oncology research · 2026Article
- Computational Metagenomics: State of the Art.International journal of molecular sciences · 2025Review
- Perception and Understanding of Artificial Intelligence Among Gastroenterology Fellows and Early Career Gastroenterologists: A Nationwide Cross-Sectional Survey Study.Digestive diseases and sciences · 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
- Artificial intelligence utilization in cancer screening program across ASEAN: a scoping review.BMC cancer · 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
- The impact of dietary fiber on colorectal cancer patients based on machine learning.Frontiers in nutrition · 2025Article
- Colorectal cancer prognosis based on dietary pattern using synthetic minority oversampling technique with K-nearest neighbors approach.Scientific reports · 2024Article
- CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in Oncology.Cancers · 2024Article
- Machine learning and deep learning for the diagnosis and treatment of ankylosing spondylitis- a scoping review.Journal of clinical orthopaedics and trauma · 2024Article
- Harnessing the Potential of Real-World Evidence in the Treatment of Colorectal Cancer: Where Do We Stand?Current treatment options in oncology · 2024Review
- Few-shot Tumor Bud Segmentation Using Generative Model in Colorectal Carcinoma.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions.Frontiers in microbiology · 2024Review
- Artificial intelligence: clinical applications and future advancement in gastrointestinal cancers.Frontiers in artificial intelligence · 2024Review
Corrections and comments
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundColorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide. Active health screening for CRC yielded detection of an increasingly younger adults. However, current machine learning algorithms that are trained using older adults and smaller datasets, may not perform well in practice for large populations.
aimTo evaluate machine learning algorithms using large datasets accounting for both younger and older adults from multiple regions and diverse sociodemographics.
methodsA large dataset including 109,343 participants in a dietary-based colorectal cancer ase study from Canada, India, Italy, South Korea, Mexico, Sweden, and the United States was collected by the Center for Disease Control and Prevention. This global dietary database was augmented with other publicly accessible information from multiple sources. Nine supervised and unsupervised machine learning algorithms were evaluated on the aggregated dataset.
resultsBoth supervised and unsupervised models performed well in predicting CRC and non-CRC phenotypes. A prediction model based on an artificial neural network (ANN) was found to be the optimal algorithm with CRC misclassification of 1% and non-CRC misclassification of 3%.
conclusionsANN models trained on large heterogeneous datasets may be applicable for both younger and older adults. Such models provide a solid foundation for building effective clinical decision support systems assisting healthcare providers in dietary-related, non-invasive screening that can be applied in large studies. Using optimal algorithms coupled with high compliance to cancer screening is expected to significantly improve early diagnoses and boost the success rate of timely and appropriate cancer interventions.
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