Accuracy Assessment of Machine Learning Algorithms Used to Predict Breast Cancer

Machine learning (ML) was used to develop classification models predict individual tumor patients’ outcomes. Binary defined whether the malignant or benign. This paper presents a comparative analysis of machine algorithms for breast cancer prediction. study dataset obtained from National Cancer Institute (NIH), USA, which contains 1.7 million data records. Classical and deep methods were included in accuracy assessment. decision tree (DT), linear discriminant (LD), logistic regression (LR), support vector (SVM), ensemble techniques (ET) used. Probabilistic neural network (PNN), (DNN), recurrent (RNN) comparison. Feature selection its effect on also investigated. The results showed that trees outperformed other techniques, as they both achieved 98.7% accuracy.