An Efficient Breast Cancer Detection Using Machine Learning Classification Models

Abstract

Breast cancer is still a dangerous and common disease that affects women all over the world, which highlights how crucial early identification is to better patient outcomes. In recent years, utilizing machine learning (ML) algorithms has improved accuracy and efficiency dramatically in a variety of applications, showing promising outcomes. This article provides a novel machine-learning approach to increase the accuracy of breast cancer detection. To improve diagnostic efficiency and accuracy, our suggested methodology combines sophisticated feature selection strategies, reliable classification algorithms, and enhanced model training methodologies. We investigated several ML classifiers, and after thorough hyperparameter tuning, the models were. Random forest and gradient boosting have achieved the highest performance with an accuracy of 97.90% and an ROC score of 0.99. This research highlights the effectiveness of ML, particularly the random forest algorithm, in breast cancer diagnosis and prognosis. Future work may explore deep learning techniques for determining the disorder’s severity.