Cloud based Breast Cancer Detection using Machine Learning by XGboost Method

Abhishek Singh, Shajid Ansari and Parineeta Jha

International Journal of Scientific Research in Artificial Intelligence and Machine Learning, 2026, vol. 2, issue 3, 179-196

Abstract: 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.

Keywords: Breast Cancer; XGBoost; Machine Learning; Classification; Early Diagnosis; Medical Data Mining; Computer-Aided Diagnosis (search for similar items in EconPapers)
Date: 2026
Note: Article URL: https://ijsraiml.com/home/article/view/IJSRAIML262410
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Persistent link: https://EconPapers.repec.org/RePEc:jbo:ijsrml:v2:y2026:i3:id:72

DOI: 10.32628/IJSRAIML262410

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