The Role of Artificial Intelligence in Early Tumor Detection: An XGBoost Risk Assessment Model for Egyptian Patients

Abstract

This study developed an XGBoost-based risk assessment model to enhance early tumor detection among Egyptian patients, addressing the challenges of late diagnosis and limited healthcare resources. Utilizing a retrospective dataset of 178 patients, the model incorporated demographic, clinical, and biochemical variables, including AFP levels, viral hepatitis status (HBV/HCV), and liver function markers. The model demonstrated strong predictive performance, achieving an accuracy of 0.833, precision of 0.846, and an AUC of 0.86, though recall remained moderate (0.688), indicating room for improvement in identifying high-risk cases. Feature importance analysis highlighted AFP levels and hepatitis status as the most influential predictors, aligning with Egypt’s high prevalence of liver cancer. The findings underscore the potential of AI-driven tools for early cancer screening in resource-limited settings, offering a scalable and cost-effective solution. However, future work should focus on expanding datasets, optimizing recall, and validating the model across diverse populations to ensure clinical applicability. This research contributes to the growing integration of AI in oncology, providing a framework for tailored risk stratification in high-burden regions

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