Breast Cancer Prediction Using Machine Learning Algorithm

In medical research, it is critical task to predict cancer in its initial stages due to the growth of population. There is a requirement of a system that can predict the cancer in early stages because in later stages, sometimes it leads to death. This paper emphasizes on breast cancer prediction in its early stages. The initial phase of the help-seeking process, when the patient recognizes and categorizes the symptoms. It is the most crucial for women with breast cancer. The reduction in the survival rate in this disease are associated to symptoms appearing three months or more later. Machine Learning has become a buzzword nowadays for the predictions of distinct types of cancers. Because of improved survival from a combination of earlier detection, breast screening and better treatment, there has been a recent decline in the number of fatalities from breast cancer. This prediction minimizes the hospital visits in future and provides the people hassle free life. The purpose of this paper is to suggest efficient algorithms that predict the breast cancer efficiently. The popular algorithms such as random forest, k-nearest neighbor, naïve bayes, and support vector machine have been applied on the popular Wisconsin dataset on breast cancer and offers good results in terms of performance metrics.