Accurate diagnosis and prediction is very important for appropriate disease treatment. Cancer is a leading cause of death worldwide, almost a million people around the globe die due to cancer every year. Cancer mortality can be reduced if it is diagnosed and treated at an early stage to save lives of cancer patients avoiding delays in care. This can be achieved with the help of machine learning. Machine learning techniques like Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs), Random Forest (RMs) and Decision Trees (DTs) is broadly being used in cancer research to develop predictive models for effective and accurate prediction of cancer. We presented a review of recent ML approaches used in the modeling of cancer progression and prediction.

Keywords : Machine Learning (ML), Artificial Neural Networks (ANN), Bayesian Networks (BN), Support Vector Machines (SVM), Decision Trees (DT).