Breast Cancer Prognosis And Detection: A Comparative Study Of Supervised Machine Learning Approaches

Abstract

Cancer is one of the most dreadful disease taking heavy toll of human life in spite of advances in the field of medical science. Among all type of cancer, Breast Cancer is amongst the most usual category affecting women everywhere in the world and it is
amid the foremost reason of death toll in women. A careful selection of techniques are required to analyse data and generate accurate results. Efficient techniques and methods are required to analyse data for accurate decision making and prediction. Machine
Learning algorithms has achieved a bench mark when examination of data set is concerned for predictive analytics. Researchers and scientific community are working to achieve higher accuracy rate to predict ailments like breast cancer. Every technique and
algorithm provide varying accuracy for different data sets and tools. In this study we will do a comparative investigation of different algorithms to find the highly appropriate and accurate breast cancer prediction algorithm. Algorithms like KNN, Decision Tree, SVM,
Random forest are being used for the study.