Published Paper Details:

Breast cancer is a type of tumour that occurs in the tissues of the breast. It is most common type of cancer found in women around the world and it is among the leading causes of deaths in women. This work presents the comparative analysis of machine learning, deep learning and data mining techniques being used for the prediction of breast cancer. Two algorithm KMEANS, PCA (Principal Component Analysis) which predict the breast cancer outcome have been compared in the paper using Kaggle machine learning repository dataset. The datasets consist 598 rows and 30 columns. All experiments are executed within a simulation environment and conducted in R studio platform. Many researchers have put their efforts on breast cancer diagnoses and prognoses, every technique has different accuracy rate and it varies for different situations, tools and datasets being used. Our main focus is to comparatively analyse different existing Machine Learning (ML) and Data Mining (DM) techniques in order to find out the most appropriate method that will support the large dataset with good accuracy of prediction. The main purpose of this research is to highlight all the previous studies of machine learning algorithms that are being used for breast cancer prediction and this project provides the all-necessary information to the beginners who want to analyse the machine learning.