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Published on in Vol 11 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/72665, first published .
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Comparison of Machine Learning Models for Colon Cancer Survival: Predictive Modeling Approach

Comparison of Machine Learning Models for Colon Cancer Survival: Predictive Modeling Approach

Journals

  1. Bamba Y, Itabashi M, Kobayashi H, Kotake K, Kawasaki M, Kanemitsu Y, Kinugasa Y, Ueno H, Maeda K, Suto T, Funahashi K, Ozawa H, Koyama F, Noura S, Ishida H, Ohue M, Kiyomatsu T, Ishihara S, Koda K, Baba H, Kawada K, Hashiguchi Y, Goi T, Toiyama Y, Tomita N, Sunami E, Akagi Y, Watanabe J, Hakamada K, Nakayama G, Sugihara K, Ajioka Y. Prognostic Power of Ensemble Learning in Colorectal Cancer with Peritoneal Metastasis: A Multi-Institutional Analysis. Bioengineering 2026;13(4):434 View
  2. Tun H, Naing L, Malik O, Abdullah M, Ta T, Rahman H. Explainable Machine Learning Based Prediction of Progression-Free Survival in Prostate Cancer: A Retrospective Cohort Study (Preprint). JMIR Cancer 2026 View
  3. Steele S, Mazengenya P, Chambuso R. Pathology-derived clinical micro-architectural diagnostics of tumour-microbiome interactions in colorectal cancer. Journal of Translational Medicine 2026;24(1) View
  4. Shankar R, Shankar R, Panicker R. Interpretable Colorectal Cancer Recurrence Risk Prediction Using a Stacked Cox Survival Framework With Counterfactual Intervention Analysis. IEEE Access 2026;14:107603 View

Conference Proceedings

  1. Gardašević G, Grbić M, Ždralević M, Mihailovic A. 2026 9th International Balkan Conference on Communications and Networking (Balkancom). Towards an Integrated CRC Risk Prediction using Machine Learning - Driven Digital Twin Framework View