Comparing the Functionality of Predicting Models for Breast Cancer Recurrence Based on Data Mining Techniques

After applying breast cancer treatment methods, there is a possibility of recurrence of the disease. The aim of the present study was using data mining techniques in order to provide predicting models for breast cancer recurrence.
18 features of 809 patients were used in the current descriptive study. The study consisted of two phases, preprocessing phase and model learning. Expectation Maximization (EM) and Classification and Regression (C and R) were used for the analysis of the first phase. In order to analyze the second phase, the five algorithm model including Neural Network, C and R, the decision tree algorithm C5.0, Bayes Net, and Support Vector Machine (SVM) was used.
The accuracy of the EM and C and R algorithms was 0.641 and 0.420, respectively, in the preprocessing phase. The accuracy of Neural Network, C and R, the decision tree algorithm C5.0, Bayes Net, and SVM algorithms was 0.858, 0.865, 0.870, 0.883, and 0.998, respectively, for the model learning phase.
According to the findings, the model with the application of EM algorithm in the first phase and SVM algorithm in the second phase had the highest functionality. It was also important in determining the treatment process.