Breast Cancer Prediction Using Machine Learning Algorithms

Author(s)	Saritha Kondapally
Country	United States
Abstract	Breast Cancer Prediction Using Machine Learning Algorithms: A Comparative Study of Artificial Neural Networks (ANN) and Naïve Bayes (NB) evaluates the performance of two machine learning algorithms—Artificial Neural Networks (ANN) and Naïve Bayes (NB)—in predicting breast cancer. By leveraging both continuous and discrete datasets, we compare the predictive accuracy and error rates of these algorithms. The findings show that ANN achieves a higher accuracy of 98%, outperforming NB (92%) in breast cancer detection. This paper also explores how discrete datasets enhance the overall forecasting performance of machine learning models and offers insights into the choice of algorithms for medical predictions.
Field	Computer > Data / Information
Published In	Volume 6, Issue 6, November-December 2024
Published On	2024-12-07
DOI	https://doi.org/10.36948/ijfmr.2024.v06i06.32607
Short DOI	https://doi.org/g8t3h7

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International Journal For Multidisciplinary Research