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Machine Learning for Breast Cancer Diagnosis by rak7848 is a document available to read on EtoBox.

The study compares various machine learning algorithms for breast cancer classification using the Wisconsin Breast Cancer Dataset. The Gradient Boosting Classifier (GBC) achieved the highest accuracy at 99.12%, while XgBoost (XGB) had the lowest at 88.10%, with overall accuracy rates for the algorithms ranging from 88-95%. The findings highlight the effectiveness of GBC in tumor classification and suggest the potential for machine learning in predicting different cancer types.

Author

rak7848

Language

EN