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This paper is in the following e-collection/theme issue:

Cancer Survivorship (376) Secondary Use of Clinical Data for Research and Surveillance (504) Machine Learning (2306) Cancer Prognosis Models and Machine Learning (89) Colon and Rectal Cancer (55)

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

Authors of this article:

Reuben Adatorwovor1 Author Orcid Image ;   Motolani E Ogunsanya2 Author Orcid Image ;   Bin Huang3 Author Orcid Image ;   Richard Charnigo4 Author Orcid Image ;   Olufunmilola Abraham5 Author Orcid Image
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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