CureMate: A clinical decision support system for breast cancer treatment

Background: Breast Cancer (BC) poses significant challenges in treatment decision-making. Multiple first treatment lines are currently available, determined by several patient-specific factors that need to be considered in the decision-making process. urpose: To present CureMate, a Clinical Decision Support System to predict the most effective initial treatment for BC patients. Different artificial intelligence models based on demographic, anatomopathological and magnetic resonance imaging variables are studied. CureMate's web application allows for easy use of the best model. Methods: A database of 232 BCE patients, each described by 29 variables, was established. Out of four machine learning algorithms, specifically Decision Tree Classifier (DTC), Gaussian Na & iuml;ve Bayes (GNB), k-Nearest Neighbor (K-NN), and Support Vector Machine (SVM), the most suitable model for the task was identified, optimized and independently tested. Results: SVM was identified as the best model for BC treatment planning, resulting in a test accuracy of 0.933. CureMate's web application, including the SVM model, allows for introducing the relevant patient variables and displays the suggested first treatment step, as well as a diagram of the following steps. Conclusion: The results demonstrate CureMate's high accuracy and effectiveness in clinical settings, indicating its potential to aid practitioners in making informed therapeutic decisions.