Multi-Layer Stacking Ensemble Learning Framework Utilizing Gut Microbiota Data for Colorectal Cancer Classification

The gut microbiota has been increasingly recognized as a promising non-invasive biomarker source for Colorectal Cancer (CRC) detection. In this study, we develop a multi-layer stacking ensemble learning framework that integrates multiple machine learning models to improve the classification accuracy of CRC based on gut microbiota profiles. Our framework is trained using publicly available microbiome datasets comprising 1129 samples from diverse geographical regions (Europe, America, and Asia), and independently evaluated on an external validation cohort collected from Peking Union Medical College Hospital (PUMCH) in China. Based on our experiments, feature extraction of gut microbiota at the genus and species levels is found to achieve the best performance. The framework integrates multiple base classifiers—including Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), Random Forests (RF), and Support Vector Machines (SVM)—combined through a weighting layer to optimize final classifications. Analysis of the feature importance in the trained model reveals several microbial populations previously reported to be associated with CRC, such as Gemella morbillorum and Fusobacterium nucleatum. These findings support the microbiological interpretability of our proposed framework. Experimental results show that our ensemble model achieves an Area Under the Receiver Operating Characteristic Curve (namely ROC_AUC) of 77.04% when validated on a real-world clinical dataset from Peking Union Medical College Hospital, surpassing existing microbiome-based CRC classification approaches.