Development and evaluation of explainable machine learning models for colorectal cancer classification using multicenter hospital data in Thailand

Background Evidence on explainable machine learning (ML) for colorectal cancer (CRC) classification using multicenter routine hospital data remains limited, particularly in Southeast Asia. Objective To develop and evaluate explainable ML models for CRC classification using multicenter routine hospital data in Thailand. Methods A retrospective multicenter case–control study was conducted across 11 provincial hospitals between June 2016 and June 2017, including 504 newly diagnosed CRC cases and 997 controls. The dataset was randomly divided into training (70%) and testing (30%) sets. Model robustness was assessed using five-fold cross-validation and repeated analyses across five random seeds. Adaptive Synthetic Sampling (ADASYN) was applied to address class imbalance. Logistic Regression, K-Nearest Neighbor, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and AdaBoost models were evaluated. Model interpretability was assessed using Shapley Additive Explanations (SHAP). Results Tree-based ensemble models demonstrated superior classification performance compared with traditional ML approaches. In the ADASYN with LASSO feature selection, XGBoost achieved the highest overall performance (accuracy 0.94 ± 0.006; AUC 0.98 ± 0.002; sensitivity 0.91 ± 0.005; specificity 0.95 ± 0.007), while RF showed comparable discrimination performance (accuracy 0.93 ± 0.003; AUC 0.98 ± 0.001). SHAP analysis identified hypertension, blood pressure measures, fasting plasma glucose and behavioral indicators as influential features associated with case–control classification. Conclusions Tree-based ensemble models demonstrated strong and robust performance for CRC classification using routine hospital data. SHAP analysis identified key socioeconomic, metabolic, clinical, and behavioral features associated with classification. External and prospective validation studies are needed to assess generalizability and support future implementation.