Breast Cancer Prediction Using Machine Learning Techniques
- https://doi.org/10.2991/ahis.k.210913.043How to use a DOI?
- IDC (Invasive Ductal Carcinoma), FNA (Fine Needle Aspirate), Breast cancer prediction, Classifier algorithms, CNN (Convolutional neural network)
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 Naïve 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.
- © 2021, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - V Apoorva AU - H K Yogish AU - M L Chayadevi PY - 2021 DA - 2021/09/13 TI - Breast Cancer Prediction Using Machine Learning Techniques BT - Proceedings of the 3rd International Conference on Integrated Intelligent Computing Communication & Security (ICIIC 2021) PB - Atlantis Press SP - 348 EP - 355 SN - 2589-4900 UR - https://doi.org/10.2991/ahis.k.210913.043 DO - https://doi.org/10.2991/ahis.k.210913.043 ID - Apoorva2021 ER -