Boosting algorithms in breast cancer classification: accuracy and performance metrics analysis

Breast cancer is the most prevalent cancer among women all over the world, so its early detection is of great significance. Th e traditional methods of detection, while effective, are quite inaccurate. This research evaluates the performance of various boosting algorithms, including AdaBoost, CatBoost, Histogram-Based Gradient Boosting, LightGBM, XGBoost, and Gradient Boosting using the Breast Cancer Wisconsin Original dataset. Our methodology encompassed data exploration, pre- processing, model training, and analysis of results. Among them, the HistGradientBoost model proved to be one of the efficient ones with very high accuracy, precision, recall, and a well-balanced F1-score, besides its excellent AUC score. These results set a great clinical significance for the application of HistGradientBoost in the diagnosis of breast cancer. In addi tion, this dataset will be expanded, integrated with deep learning techniques, and hyperparameters will be optimized further to enhance the performance of models in the near future. This can provide valuable input to data scientists and medical practitioners in selecting appropriate machine learning models for the diagnosis of breast cancer to arrive at an early diagnosis and prevent poor outcomes.