Interpretable Machine Learning Models Based on Blood Cell-Derived Inflammatory Indices for Identifying Colorectal Neoplasia: A Retrospective Study

Purpose

Noninvasive approaches for the risk assessment of colorectal neoplasia remain limited, particularly in terms of interpretability and clinical applicability. This study aimed to develop and validate interpretable machine learning models using blood cell-derived inflammatory indices to identify colorectal adenoma (CRA) and colorectal cancer (CRC). Patients and methods: This retrospective study included 1301 participants from the Second Affiliated Hospital of Guangxi Medical University between January 2017 and April 2022, comprising 487 healthy controls (HC), 327 patients with CRA, and 487 patients with CRC. Restricted cubic spline (RCS) analysis was performed to evaluate the associations between inflammatory indices and colorectal neoplasia. Least absolute shrinkage and selection operator regression and the Boruta algorithm were used for feature selection. Five machine learning algorithms were applied and compared: logistic regression, support vector machine, k-nearest neighbors, random forest, and extreme gradient boosting (XGB). Model performance was evaluated using receiver operating characteristic analysis, calibration curves, and decision curve analysis. SHapley Additive exPlanations (SHAP) was applied to enhance interpretability.