Machine Learning Methods for Breast Cancer Diagnosis

Breast cancer is an important issue in the field of women's health. Early detection and diagnosis are crucial to treatment and prognosis. However, traditional breast cancer detection methods have some limitations, and it is challenging to accurately identify tumors. Machine learning technology can play an important role in early diagnosis of breast cancer. This paper aims to develop an accurate and efficient machine learning model that can use clinical data to automatically detect breast cancer, so as to judge whether the tumor is malignant or benign. In this study, we explore the application of machine learning algorithms in the classification of breast cancer using the Wisconsin Breast Cancer Database. A machine learning framework is proposed. Leveraging the rich clinical data provided by this database, we employ various classifiers, including KNN, Random Forest, SVM, Logistic Regression, and Naive Bayes, to develop AI algorithm for breast cancer detection. This paper implements and compare this machine learning algorithms based on their performance metrics, such as accuracy, precision, recall, and F-measure. Experimental results indicate that SVM outperforms other classifiers, achieving high precision, recall, and F-measure values for both malignant and benign samples. This result suggests that SVM is well-suited for breast cancer detection tasks and holds significant potential for clinical applications.