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An ensemble machine learning approach for predicting colorectal polyps using novel inflammatory markers
Linxiao Zhang1, Yingfei Wu1, Qi Jin1
1Department of Gastroenterology, The Second Qilu Hospital of Shandong University, at 247 Beiyuan Road, Jinan, Shandong, 250033, China.
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.