DATA SHEET.csv

Breast cancer is one of the most prevalent cancers among women worldwide, and early detection is crucial for reducing mortality rates and improving treatment outcomes. Mammography has been the gold standard for breast cancer screening, offering non-invasive imaging to identify suspicious abnormalities. However, mammography has limitations, such as variability in interpretation, false positives, false negatives, and challenges in distinguishing between benign and malignant lesions.Machine learning has the potential to revolutionize breast cancer detection by enhancing the capabilities of mammography. Its ability to improve accuracy, efficiency, and consistency in diagnosis makes it an indispensable tool for early detection efforts.This study focuses on developing a machine learning-based predictive model for the early detection and classification of breast cancer, utilizing the Wisconsin Breast Cancer Diagnostic dataset. Special emphasis is placed on the potential of ML algorithms, particularly the Support Vector Classifier with a Radial Basis Function (SVC-RBF), to enhance diagnostic accuracy and efficiency.Machine learning has the potential to revolutionize breast cancer detection by enhancing the capabilities of mammography. Its ability to improve accuracy, efficiency, and consistency in diagnosis makes it an indispensable tool for early detection efforts.乳腺癌是全球女性群体中高发的恶性肿瘤之一，早期检测对于降低患者死亡率、改善治疗预后具有关键意义。 乳腺钼靶摄影（Mammography）长期以来作为乳腺癌筛查的金标准，可通过无创影像学检查检出可疑异常病变，但该技术存在诸多局限：阅片结果存在个体差异、假阳性与假阴性率较高，且难以准确区分良性与恶性病灶。 机器学习（Machine Learning）有望通过增强乳腺钼靶摄影的诊断效能，革新乳腺癌检测模式；其在提升诊断准确性、工作效率与结果一致性方面的优势，使其成为乳腺癌早期筛查工作中不可或缺的核心工具。 本研究以威斯康星乳腺癌诊断数据集（Wisconsin Breast Cancer Diagnostic dataset）为数据基础，旨在开发一款用于乳腺癌早期检测与分型的机器学习预测模型，并重点探讨了机器学习算法的应用潜力——尤其是搭载径向基核函数的支持向量分类器（Support Vector Classifier with a Radial Basis Function, SVC-RBF）在提升诊断准确性与效率方面的价值。 机器学习有望通过增强乳腺钼靶摄影的诊断效能，革新乳腺癌检测模式；其在提升诊断准确性、工作效率与结果一致性方面的优势，使其成为乳腺癌早期筛查工作中不可或缺的核心工具。