Breast Cancer Prediction Using Machine Learning Techniques

Abstract

Breast cancer affects the majority of women worldwide, and it is the second most common cause of death among women. However, if cancer is detected early and treated properly, it is possible to be cured of the condition. Early detection of breast cancer can dramatically improve the prognosis and chances of survival by allowing patients to receive timely clinical therapy. Furthermore, precise benign tumour classification can help patients avoid unneeded treatment. This paper study uses Convolution Neural Networks for Image dataset and K-Nearest Neighbour (KNN), Decision Tree (CART), Support Vector Machine (SVM), and Nave Bayes for numerical dataset, whose features are obtained from digitised image of breast mass, as to forecast and analyse cancer databases in order to improve accuracy. The dataset will be analysed, evaluated, and model is trained as part of the process. Finally, both image and numerical test data will be used for prediction.