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：癌症是全球女性致死率最高的疾病之一，其中乳腺癌的发病率和死亡率均居首位。然而，通过早期检测及相应治疗可有效预防该疾病。任何针对此类癌症检测或预测的技术进展对提升健康生活质量均具有重要意义。现有研究多聚焦于构建高准确率的癌症预测模型，但仅凭准确率有时并非可靠评估指标。本研究采用探究性方法，基于提升（boosting）框架比较不同机器学习算法在乳腺癌预测中的性能，重点关注召回率指标。提升类机器学习算法已被证明是检测医疗疾病的有效工具。本研究采用加州大学欧文分校（UCI）数据库中的数据集对分类器进行训练与测试，该数据集包含乳腺癌相关特征属性。本研究主要目标在于运用前沿提升算法（包括AdaBoost、XGBoost、CatBoost和LightGBM）预测和诊断乳腺癌，并通过召回率、ROC-AUC及混淆矩阵等指标评估其有效性。此外，本研究首次联合使用上述四种提升算法与超参数优化库Optuna，并引入SHAP方法增强模型可解释性，构建的模型可作为乳腺癌识别与预测的辅助工具。实验成功提升了所有模型的AUC或召回率指标，其中AdaBoost和LightGBM的假阴性率显著降低，所有模型的最终AUC均超过99.41%。