Cancer Survival Analysis Using Machine Learning

Abstract

To improve the quality of life and increase the survival rate of individuals with cancer, early treatment and detection plays a crucial role. Early detection and diagnosis pose almost 100 percent survival rate, especially before or during stage-I. However, situations, where the detection comes during stage-IV, the rate of survival is as low as 30 percent. The quest to foster early detection has paved the way for the evolution of machine learning techniques and have emerged in response to cancer’s big data. This paper has examined Machine Learning (ML) predictive models that have been applied in the early detection of cancer, providing some of the benefits and drawbacks with which they are associated. From the results documented in most of the current literature, the ML techniques pose remarkable improvements in the prediction and classification accuracy of cancer. The implication is that in future, healthcare systems ought to combine various ML techniques with multidimensional heterogeneous data to produce more accurate results regarding cancer prediction and classification.