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Breast Cancer SVM Classification Study by cseashwini is a document available to read on EtoBox.

What is Breast Cancer SVM Classification Study about?

This project focuses on using Support Vector Machines (SVM) to classify breast cancer tumors as malignant or benign, achieving an impressive accuracy of 97% compared to the traditional 79% accuracy of manual diagnoses. The methodology includes data preprocessing, model training, and evaluation using the Breast Cancer Wisconsin dataset, demonstrating the effectiveness of machine learning in improving diagnostic accuracy. Future work may explore deep learning techniques and real-time clinical data integration

Author

cseashwini

Language

EN