An ensemble machine learning approach for predicting colorectal polyps using novel inflammatory markers.
- 1Department of Gastroenterology, The Second Qilu Hospital of Shandong University, at 247 Beiyuan Road, Jinan, Shandong, 250033, China.
- 2Department of Gastroenterology, The Second Qilu Hospital of Shandong University, at 247 Beiyuan Road, Jinan, Shandong, 250033, China. [email protected].
Abstract
Objectives
This study aimed to investigate the associations of two novel inflammatory-nutritional markers, the C-reactive Protein-Albumin-Lymphocyte (CALLY) index and C-reactive protein-to-lymphocyte ratio (CLR), with colorectal polyps, and to develop machine learning models for improved risk prediction.Methods
The study conducted a retrospective analysis of 946 patients who underwent colonoscopy at the Second Qilu Hospital of Shandong University from 2023 to 2024. Clinical, laboratory, and endoscopic data were retrieved. Four machine learning models were developed: Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Additionally, a Stacking ensemble model was constructed, integrating RF, XGBoost, and SVM with logistic regression as the meta-learner. Model performance was evaluated using metrics including the Area Under the Curve (AUC), accuracy, precision, recall, F1-score, and Kappa coefficient.Results
Both the CALLY index and CLR were significantly associated with the presence of colorectal polyps. The Stacking ensemble model achieved the highest predictive performance (AUC: 0.96), outperforming all individual base models. The RF, SVM, and XGBoost models also attained robust predictive capability (AUC: 0.75-0.76). By comparison, LightGBM exhibited suboptimal performance (AUC = 0.72). Feature importance analysis revealed CA19-9 as the top predictive factor.Conclusion
Elevated CLR and reduced CALLY index, together with traditional risk factors, synergistically contribute to colorectal polyp formation. The Stacking ensemble model serves as a highly accurate tool for identifying individuals at high risk of colorectal polyps, thereby facilitating targeted screening and timely early intervention. These findings support the incorporation of multi-dimensional inflammatory-nutritional biomarkers and machine learning strategies into clinical practice to optimize Colorectal Cancer (CRC) prevention.
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BibTeX
@article{zhang2026ensemble,
title = {{An ensemble machine learning approach for predicting colorectal polyps using novel inflammatory markers.}},
author = {Linxiao Zhang and Yingfei Wu and Qi Jin and Ying Wen and Yuxi Wang and Han Qi and Xiaoning Zhang and Weihua Yu},
journal = {BMC gastroenterology},
year = {2026},
month = jul,
doi = {10.1186/s12876-026-05106-8},
url = {https://europepmc.org/article/MED/42894111},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - An ensemble machine learning approach for predicting colorectal polyps using novel inflammatory markers.
AU - Linxiao Zhang
AU - Yingfei Wu
AU - Qi Jin
AU - Ying Wen
AU - Yuxi Wang
AU - Han Qi
AU - Xiaoning Zhang
AU - Weihua Yu
PY - 2026
DA - 2026/07/01
JO - BMC gastroenterology
DO - 10.1186/s12876-026-05106-8
UR - https://europepmc.org/article/MED/42894111
AB - Objectives This study aimed to investigate the associations of two novel inflammatory-nutritional markers, the C-reactive Protein-Albumin-Lymphocyte (CALLY) index and C-reactive protein-to-lymphocyte ratio (CLR), with colorectal polyps, and to develop machine learning models for improved risk prediction.Methods The study conducted a retrospective analysis of 946 patients who underwent colonoscopy at the Second Qilu Hospital of Shandong University from 2023 to 2024. Clinical, laboratory, and endoscopic data were retrieved. Four machine learning models were developed: Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Additionally, a Stacking ensemble model was constructed, integrating RF, XGBoost, and SVM with logistic regression as the meta-learner. Model performance was evaluated using metrics including the Area Under the Curve (AUC), accuracy, precision, recall, F1-score, and Kappa coefficient.Results Both the CALLY index and CLR were significantly associated with the presence of colorectal polyps. The Stacking ensemble model achieved the highest predictive performance (AUC: 0.96), outperforming all individual base models. The RF, SVM, and XGBoost models also attained robust predictive capability (AUC: 0.75-0.76). By comparison, LightGBM exhibited suboptimal performance (AUC = 0.72). Feature importance analysis revealed CA19-9 as the top predictive factor.Conclusion Elevated CLR and reduced CALLY index, together with traditional risk factors, synergistically contribute to colorectal polyp formation. The Stacking ensemble model serves as a highly accurate tool for identifying individuals at high risk of colorectal polyps, thereby facilitating targeted screening and timely early intervention. These findings support the incorporation of multi-dimensional inflammatory-nutritional biomarkers and machine learning strategies into clinical practice to optimize Colorectal Cancer (CRC) prevention.
SN - 1471-230X
ER -