摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-肿瘤学的新研究-乳腺癌是一篇报道的主题。根据NewsRx记者从摩洛哥拉巴特发回的新闻报道，研究表明：“目标是使用三种不同的Machine学习模型来预测乳腺癌的复发，这三种不同的Machine学习模型来自不同疾病类型和阶段的患者，包括Trip Le阴性乳腺癌(TNBC)和非三阴性乳腺癌(non-TNBC)。”被检查过。新闻记者从拉巴特的Mohammed V Unive Rustity获得了一句研究的引文，“使用以下五种Mac Hine学习技术创建了三个不同的模型：自适应Boosting(AdaBoost)、随机欠采样Boosting(RUSBoost)、极端梯度Boosting(XGBoost)、支持向量机(SVM)、临床模型使用临床和病理数据，结合机器学习算法，机器学习算法结合动态增强磁共振成像在放射学模型中的(DCE-MRI)成像特征，合并模型结合两种类型的数据，使用多个标准对每种技术进行评价，包括受试者操作特征(ROC)曲线、精确度、召回率、查全率。结果表明，临床和放射学数据的整合提高了乳腺癌复发病例的预测精度，XGBoost算法被公认为性能最好的算法，本研究的发现对乳腺癌研究领域，特别是对乳腺癌复发的预测有重要贡献。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology - Breast Canc er is the subject of a report. According to news reporting from Rabat, Morocco, by NewsRx journalists, research stated, "The goal is to use three different mach ine learning models to predict the recurrence of breast cancer across a very het erogeneous sample of patients with varying disease kinds and stages. A heterogen eous group of patients with varying cancer kinds and stages, including both trip le-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBC ), was examined." The news correspondents obtained a quote from the research from Mohammed V Unive rsity in Rabat, "Three distinct models were created using the following five mac hine learning techniques: Adaptive Boosting (AdaBoost), Random Under-sampling Bo osting (RUSBoost), Extreme Gradient Boosting (XGBoost), support vector machines (SVM), and Logistic Regression. The clinical model used both clinical and pathol ogy data in conjunction with the machine learning algorithms. The machine learni ng algorithms were combined with dynamic contrast-enhanced magnetic resonance im aging (DCE-MRI) imaging characteristics in the radiomic model, and the merged mo del combined the two types of data. Each technique was evaluated using several c riteria, including the receiver operating characteristic (ROC) curve, precision, recall, and F1 score. The results suggest that the integration of clinical and radiomic data improves the predictive accuracy in identifying instances of breas t cancer recurrence. The XGBoost algorithm is widely recognized as the most effe ctive algorithm in terms of performance. The findings presented in this study of fer significant contributions to the field of breast cancer research, particular ly in relation to the prediction of cancer recurrence."
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