Cancer is one of the diseases that kill the most women in the world, with breast cancer being responsible for the highest number of cancer cases and consequently deaths. However, it can be prevented by early detection and, consequently, early treatment. Any development for detection or perdition this kind of cancer is important for a better healthy life. Many studies focus on a model with high accuracy in cancer prediction, but sometimes accuracy alone may not always be a reliable metric. This study implies an investigative approach to studying the performance of different machine learning algorithms based on boosting to predict breast cancer focusing on the recall metric. Boosting machine learning algorithms has been proven to be an effective tool for detecting medical diseases. The dataset of the University of California, Irvine (UCI) repository has been utilized to train and test the model classifier that contains their attributes. The main objective of this study is to use state-of-the-art boosting algorithms such as AdaBoost, XGBoost, CatBoost and LightGBM to predict and diagnose breast cancer and to find the most effective metric regarding recall, ROC-AUC, and confusion matrix. Furthermore, our study is the first to use these four boosting algorithms with Optuna, a library for hyperparameter optimization, and the SHAP method to improve the interpretability of our model, which can be used as a support to identify and predict breast cancer. We were able to improve AUC or recall for all the models and reduce the False Negative for AdaBoost and LigthGBM the final AUC were more than 99.41\% for all models.

翻译：癌症是全球导致女性死亡最多的疾病之一，其中乳腺癌在癌症病例及死亡人数中占比最高。然而，通过早期检测和及时治疗可以预防该疾病。任何针对这类癌症的检测或预测技术的开发，对提升健康生活质量都至关重要。现有研究多聚焦于构建高精度的癌症预测模型，但单纯依赖准确率指标可能存在局限性。本研究采用探索性分析方法，重点研究基于提升算法的多种机器学习模型在乳腺癌预测中的性能，特别关注召回率指标。提升类机器学习算法已被证实是医学疾病检测的有效工具。研究使用加州大学尔湾分校（UCI）数据库中的数据集进行模型训练与测试，该数据集包含乳腺肿瘤的多个特征属性。本研究的主要目标是采用当前先进的提升算法（包括AdaBoost、XGBoost、CatBoost和LightGBM）进行乳腺癌预测与诊断，并通过召回率、ROC-AUC和混淆矩阵等指标评估模型效能。值得注意的是，本研究首次将这四种提升算法与超参数优化库Optuna及SHAP方法相结合，显著提升了模型的可解释性，可作为乳腺癌识别与预测的辅助工具。经优化后，所有模型的AUC或召回率均得到提升，其中AdaBoost和LightGBM的假阴性率显著降低，最终所有模型的AUC值均超过99.41%。