Predicting recurrence of breast cancer using amalgamated machine learning algorithm

Abstract

Breast cancer is the second leading cause of cancer death in women. Though the survival ratein todays developed world is high, there is substantial possibility that it would recur. However, several computational methods have been developed to contribute for this prediction. Machine learning is one such established computational tool. The main aim of this paper is to design a model that can precisely predict the recurrence of breast cancer using assorted machine learning algorithms. For this, an amalgamated model has been trained and the results have been compared against other traditional models. Analysis of the results obtained state that the amalgamated model yields an accuracy of 87.76% whereas the highest accuracy among other trained models is 77.62%.This prediction can assist in providing better treatment for patients.