Machine Learning Algorithms for Diagnosis of Breast Cancer

Abstract

Machine learning is applied on systems for sequence and trend recognition, incomprehensible by humans or traditional programming, as they efficiently utilize the patterns for training the networks to overcome particular challenges. The prevalence of machine learning in medical sciences is devised to decrease the mortality rate of cancer patients owing to its detection at early stages. The objective of this review paper is to compare machine learning algorithms, to be precise, Support Vector Machine (SVM), Random Forest (RF), Bayesian Networks (BN) and k-Nearest Neighbor (kNN) so as to achieve precise detection and classification of breast cancer.