Machine learning analysis of breast cancer treatment protocols and cycle counts: A case study at Mohammed vi hospital, Morocco - Houda AIT BRAHIM; Salah EL-HADAJ; Abdelmoutalib METRANE (Systems and Soft Computing)
This paper presents a new study of predicting patients' breast cancer treatment protocol and the corresponding treatment cycle based on machine learning algorithms. The data used were collected at Mohammed VI Hospital in Morocco, and it contains patient information with two targets (protocol and treatment cycle).After preparing the data and testing several machine learning algorithms, two models were developed: The first one, based on Gradient Boosting Classifier algorithm, successfully classified patient treatment protocols with an overall accuracy of 64 % across all categories and an impressive 94 % accuracy for the mode category, widely adopted in the hospital. The second model, based on Random Forest Regressor algorithm, which integrates the results of the first model during the training, predicted the treatment cycle of patients with a Root Mean Square Error (RMSE) score of 0.050 and a Mean Absolute Percentage Error (MAPE) score of 0.020. Furthermore, feature importance analysis was performed to highlight the importance of variables, and show the positive influence of some variables on the models.Finally, this study can help doctors quickly make decisions about the treatment needed for each patient and also gives an idea of which molecules should exist in the hospital stock based on the patient's treatment cycle predicted.