• Rishabh Khosla

Breast Cancer is the most often identified cancer among women and major reason for increasing mortality rate among women. As the diagnosis of this disease manually takes long hours and the lesser availability of systems, there is a need to develop the automatic diagnosis system for early detection of cancer. Data mining techniques contribute a lot in the development of such system. For the classification of benign and malignant tumour I have used classification techniques of machine learning in which the machine learns from the past data and can predict the category of new input. This is a relative study on the implementation of models using Random Forest, K nearest neighbours and SVM. This analysis aims to observe which features are most helpful in predicting malignant or benign cancer and to see general trends that may aid us in model selection and hyper parameter selection. The goal is to classify whether the breast cancer is benign or malignant. To achieve this machine learning classification methods have been used to fit a function that can predict the discrete class of new input.

Breast Cancer KNN SVM Random Forest Machine Learning

"Breast Cancer Detection Using Random Forest, KNN and SVM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 2, page no.784-787, February-2020, Available :http://www.jetir.org/papers/JETIR2002327.pdf

"Breast Cancer Detection Using Random Forest, KNN and SVM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 2, page no. pp784-787, February-2020, Available at : http://www.jetir.org/papers/JETIR2002327.pdf