A Comparative Study of Machine Learning Techniques to Predict Types of Breast Cancer Recurrence

The prediction of breast cancer recurrence is a crucial problem in research that requires accurate and efficient models. This study aims to compare the performance different machine learning techniques predicting types recurrence. In this study, logistic regression, decision tree, K-Nearest Neighbors, artificial neural network algorithms was compared on dataset. results show algorithm outperformed other with 91% accuracy, followed by tree (DT) Neighbors (kNN) also performed well accuracies 90.10% 88.20%, respectively, while regression had lowest accuracy 84.60%. provide insight into effectiveness could guide development more