Comparative study of machine learning algorithms for breast cancer detection and diagnosis

Abstract

Breast cancer is one of the most widespread diseases among women in the UAE and worldwide. Correct and early diagnosis is an extremely important step in rehabilitation and treatment. However, it is not an easy one due to several uncertainties in detection using mammograms. Machine Learning (ML) techniques can be used to develop tools for physicians that can be used as an effective mechanism for early detection and diagnosis of breast cancer which will greatly enhance the survival rate of patients. This paper compares three of the most popular ML techniques commonly used for breast cancer detection and diagnosis, namely Support Vector Machine (SVM), Random Forest (RF) and Bayesian Networks (BN). The Wisconsin original breast cancer data set was used as a training set to evaluate and compare the performance of the three ML classifiers in terms of key parameters such as accuracy, recall, precision and area of ROC. The results obtained in this paper provide an overview of the state of art ML techniques for breast cancer detection.

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