AI Support System for Predicting Colorectal Cancer Using Xgboost

Early and accurate diagnosis of colorectal cancer (CRC) significantly improves patient survival rates. This study introduces a predictive support system leveraging XGBoost and Convolutional Neural Networks (CNN) for CRC classification. Structured data, including blood test results and patient demographics, is analyzed using XGBoost, while colonoscopy images are processed through CNN for enhanced feature detection. The integrated model was trained and validated on diverse datasets, achieving a predictive accuracy exceeding 92%. The proposed system is designed to be scalable, interpretable, and applicable in real-time clinical settings. This research highlights the effectiveness of combining machine learning techniques with clinical diagnostics for early-stage colorectal cancer detection.