Breast Cancer Prediction Using Machine Learning Techniques

DOI

10.2991/ahis.k.210913.043How to use a DOI?

Keywords

IDC (Invasive Ductal Carcinoma), FNA (Fine Needle Aspirate), Breast cancer prediction, Classifier algorithms, CNN (Convolutional neural network)

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 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.

Copyright

© 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/).

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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  - 10.2991/ahis.k.210913.043
ID  - Apoorva2021
ER  -