A Comprehensive Analysis on the Efficacy of Machine Learning-Based Algorithms for Breast Cancer Classification

Abstract

This research focuses on using machine learning to make breast cancer classification better. In this research various machine learning algorithm such as Decision Tree, Linear Discriminant, Support Vector Machine, K-Nearest Neighbors, Probabilistic Neural Network, Logistic Regression, Recurrent Neural Network, and Ensemble Method are used. We tested them using two different ways of splitting the data—90/10 and 70/30—and we also picked important features to consider. The Ensemble Method came in second place with accuracies of 98.2% and 97.6%. The Deep Neural Network performed really well too, with accuracies of 96.2% in the 90/10 split and 89.1% in the 70/30 split. We also found that selecting the right features improved accuracy a lot. This shows how important it is to choose the best features to make the models better. These results show that machine learning can be used to classify breast cancer effectively. The numbers prove that the Deep Neural Network and Ensemble methods have high accuracy, and selecting the right features makes them even better. The research outcomes introduces machine learning techniques that can improve breast cancer diagnosis, potentially changing the way doctors make decisions and improving patient outcomes.