Breast Cancer Prediction Using Machine Learning Algorithms

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.