Clinical decision support for precolonoscopy cancer triage: A rule-out-oriented machine learning model for colorectal cancer risk

doi:10.4251/wjgo.121970

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World Journal of Gastrointestinal Oncology

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1948-5204

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

Retrospective Cohort Study

Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Gastrointest Oncol. Sep 15, 2026; 18(9): 121970
Published online Sep 15, 2026. doi: 10.4251/wjgo.121970

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Figure 3 Beeswarm plot showing the distribution of SHapley Additive exPlanations values for the top-10 predictors (English labels). Each dot represents an individual patient; X-axis indicates SHapley Additive exPlanations value (impact on model output), and color denotes the feature value (low to high). SHAP: SHapley Additive exPlanations; NLR: Neutrophil to lymphocyte ratio; HDL: High-density lipoprotein; SII: Systemic immune-inflammation index; NHR: Neutrophil-to-high-density lipoprotein ratio; AST: Aspartate aminotransferase; ALT: Alanine aminotransferase.

Citation: Polat YH, Kayaalp M. Clinical decision support for precolonoscopy cancer triage: A rule-out-oriented machine learning model for colorectal cancer risk. World J Gastrointest Oncol 2026; 18(9): 121970
URL: https://www.wjgnet.com/1948-5204/full/v18/i9/121970.htm
DOI: https://dx.doi.org/10.4251/wjgo.121970

Polat YH, Kayaalp M. Clinical decision support for precolonoscopy cancer triage: A rule-out-oriented machine learning model for colorectal cancer risk. World J Gastrointest Oncol 2026; 18(9): 121970 [DOI: 10.4251/wjgo.121970]

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