首页|Mohammed V University in Rabat Reports Findings in Breast Cancer (Predicting dis ease recurrence in breast cancer patients using machine learning models with cli nical and radiomic characteristics: a retrospective study)
Mohammed V University in Rabat Reports Findings in Breast Cancer (Predicting dis ease recurrence in breast cancer patients using machine learning models with cli nical and radiomic characteristics: a retrospective study)
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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."
RabatMoroccoAfricaBreast CancerC ancerCyborgsEmerging TechnologiesHealth and MedicineMachine LearningOn cologyWomen's Health
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