Cloud based Breast Cancer Detection using Machine Learning by XGboost Method

Breast cancer is among the most prevalent cancers affecting women worldwide and remains a major cause of cancer-related mortality. Early detection and accurate diagnosis are critical for improving patient survival rates and treatment outcomes. Machine learning techniques have emerged as powerful tools for supporting clinical decision-making in medical diagnosis. This study investigates the effectiveness of Extreme Gradient Boosting (XGBoost), a state-of-the-art ensemble learning algorithm, for breast cancer classification. The proposed model was trained and evaluated using a labeled breast cancer dataset containing clinical and imaging-derived features. Performance was assessed using standard classification metrics, including accuracy, precision, recall, F1-score, and Receiver Operating Characteristic–Area Under the Curve (ROC-AUC). Experimental results demonstrate that XGBoost provides robust classification performance and effectively distinguishes malignant cases from benign or normal samples. The findings highlight the potential of XGBoost as a reliable computer-aided diagnostic tool for breast cancer screening and early detection.