Data Mining Techniques for Early Detection of Breast Cancer

Maria Inês Cruz, Jorge Bernardino

2019

Abstract

Nowadays, millions of people around the world are living with the diagnosis of cancer, so it is very important to investigate some forms of detection and prevention of this disease. In this paper, we will use an ensemble technique with some data mining algorithms applied to a dataset related to the diagnosis of breast cancer using biological markers found in routine blood tests, in order to diagnose this disease. From the results obtained, it can be verified that the model got an AUC of 95% and a precision of 87%. Thus, through this model it is possible to create new screening tools to assist doctors and prevent healthy patients from having to undergo invasive examinations.

Paper Citation

in Harvard Style

Cruz M. and Bernardino J. (2019). Data Mining Techniques for Early Detection of Breast Cancer. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR; ISBN 978-989-758-382-7, SciTePress, pages 434-441. DOI: 10.5220/0008346504340441

in Bibtex Style

@conference{kdir19,
author={Maria Inês Cruz and Jorge Bernardino},
title={Data Mining Techniques for Early Detection of Breast Cancer},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR},
year={2019},
pages={434-441},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008346504340441},
isbn={978-989-758-382-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR
TI - Data Mining Techniques for Early Detection of Breast Cancer
SN - 978-989-758-382-7
AU - Cruz M.
AU - Bernardino J.
PY - 2019
SP - 434
EP - 441
DO - 10.5220/0008346504340441
PB - SciTePress