Analysis of Tumor Classification Algorithms for Breast Cancer Prediction by Machine Learning Methods

Abstract

The paper presents machine learning methods for classification and forecasting problems. Tumor classification algorithms based on the Random Forest method are considered. To understand the distribution of classified data, a 3D graph of the three attributes of the date set was implemented. For a better understanding, graphs were constructed, namely the ROC curve and the RP curve. The AUC value for the model was also determined. The results of the graphs and AUC values ​​were compared with the NoSkill model, ie the model without skills. High quality of the received models is offered.