The volume and amount of data in cancerology is continuously increasing, yet the vast majority of this data is not being used to uncover useful and hidden insights. As a result, one of the key goals of physicians for therapeutic decision-making during multidisciplinary consultation meetings is to combine prediction tools based on data and best practices (MCM). The current study looked into using CRISP-DM machine learning algorithms to predict metastatic recurrence in patients with early-stage (non-metastatic) breast cancer so that treatmentappropriate medicine may be given to lower the likelihood of metastatic relapse. From 2014 to 2021, data from patients with localized breast cancer were collected at the Regional Oncology Center in Meknes, Morocco. There were 449 records in the dataset, 13 predictor variables and one outcome variable. To create predictive models, we used machine learning techniques such as Support Vector Machine (SVM), Nave Bayes (NB), K-Nearest Neighbors (KNN) and Logistic Regression (LR). The main objective of this article is to compare the performance of these four algorithms on our data in terms of sensitivity, specificity and precision. According to our results, the accuracies of SVM, kNN, LR and NB are 0.906, 0.861, 0.806 and 0.517 respectively. With the fewest errors and maximum accuracy, the SVM classification model predicts metastatic breast cancer relapse. The unbiased prediction accuracy of each model is assessed using a 10-fold cross-validation method.

Accurately predicting effective treatment methods based on personalized tumor genetic profiles is a major goal of precision cancer medicine. Because people with breast cancer at comparable stages respond differently to treatment, it is essential to gain insight into the variables that influence treatment success. This study presents a supervised multinomial logistic regression model for predicting the best adjuvant therapy for breast cancer patients to lower the probability of metastatic recurrence. This model will assist health professionals (physicians) in making judgments about which medicinal regimens to suggest to patients. In addition, this article presents a comparison of several multinomial machine learning methods (Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Neural Network (ANN)).The results reveal that the Random Forest classifier is more effective in terms of adjuvant therapy combination prediction accuracy.

Accurately predicting effective treatment methods based on personalized tumor genetic profiles is a major goal of precision cancer medicine. Because people with breast cancer at comparable stages respond differently to treatment, it is essential to gain insight into the variables that influence treatment success. This study presents a supervised multinomial logistic regression model for predicting the best adjuvant therapy for breast cancer patients to lower the probability of metastatic recurrence. This model will assist health professionals (physicians) in making judgments about which medicinal regimens to suggest to patients. In addition, this article presents a comparison of several multinomial machine learning methods (Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Neural Network (ANN)). The results reveal that the Random Forest classifier is more effective in terms of adjuvant therapy combination prediction accuracy.