Prediction of Breast Cancer using Random Forest, Support Vector Machines and Naïve Bayes

Abstract

Machine learning techniques can be used to judge important predictor variables in medical datasets. This paper applies three machine learning techniques: Naïve Bayes, SVM and Random Forest to Wisconsin Breast Cancer Database. The three developed models predict whether the patients’ trauma are benign or malignant. The paper aims at comparing the performance of these three algorithms through accuracy, precision, recall and f-measure. Results show that Random Forest yields the best accuracy of 99.42%, which is slightly better than both SVM and Naïve Bayes that have accuracies of 98.8% and 98.24% respectively. These results are very competitive and can be used for diagnosis, prognosis, and treatment.