Publications
2021
Cueto-López, Nahúm; García-Ordás, María Teresa; Vitelli-Storelli, Facundo; Fernández-Navarro, Pablo; Palazuelos, Camilo; Alaiz-Rodríguez, Rocío
Evaluation of feature selection techniques for breast cancer risk prediction Artículo de revista
En: International Journal of Environmental Research and Public Health, vol. 18, no 20, pp. 10670, 2021, (Publisher: MDPI).
Resumen | Enlaces | BibTeX | Etiquetas: Breast Cancer, feature selection, Risk Prediction Model, stability
@article{cueto-lopez_evaluation_2021,
title = {Evaluation of feature selection techniques for breast cancer risk prediction},
author = {Nahúm Cueto-López and María Teresa García-Ordás and Facundo Vitelli-Storelli and Pablo Fernández-Navarro and Camilo Palazuelos and Rocío Alaiz-Rodríguez},
url = {https://www.mdpi.com/1660-4601/18/20/10670},
year = {2021},
date = {2021-01-01},
journal = {International Journal of Environmental Research and Public Health},
volume = {18},
number = {20},
pages = {10670},
abstract = {This study evaluates feature selection techniques combined with machine learning classifiers to improve breast cancer risk prediction. Using data from the MCC-Spain study (919 cases, 946 controls) with environmental and genetic features, the goal is to identify key risk factors and assess the stability of feature selection methods. SVM-RFE achieved the best performance, with a Logistic Regression model using its top-47 ranked features obtaining an AUC of 0.616 (5.8% improvement). SVM-RFE and Random Forest were the most stable selection methods, but SVM-RFE outperformed Random Forest in predictive accuracy.},
note = {Publisher: MDPI},
keywords = {Breast Cancer, feature selection, Risk Prediction Model, stability},
pubstate = {published},
tppubtype = {article}
}
2017
Figueroa, Jonine; Gray, Calum; Papanastasiou, Giorgos; González-Castro, Víctor; Polydorides, Nick; Andrew, Evans; Vinnicombe, Sarah
Towards the development of non-invasive measures of breast cancer risk: image analysis of digital breast tomosynthesis mammograms and tissue lobule content. Artículo de revista
En: 2017.
Resumen | Enlaces | BibTeX | Etiquetas: Breast Cancer, Digital Breast Tomosynthesis, image analysis, Mammography, Risk Assessment
@article{figueroa_towards_2017,
title = {Towards the development of non-invasive measures of breast cancer risk: image analysis of digital breast tomosynthesis mammograms and tissue lobule content.},
author = {Jonine Figueroa and Calum Gray and Giorgos Papanastasiou and Víctor González-Castro and Nick Polydorides and Evans Andrew and Sarah Vinnicombe},
url = {https://repository.essex.ac.uk/28227/1/J.Figueroa,%20%5B%5D,%20Papanastasiou,%20et%20al%20EMIM%202017.pdf},
year = {2017},
date = {2017-01-01},
abstract = {Towards the development of non-invasive measures of breast cancer risk: image analysis of digital breast tomosynthesis mammog. See discussions, stats, and author profiles for this publication.},
keywords = {Breast Cancer, Digital Breast Tomosynthesis, image analysis, Mammography, Risk Assessment},
pubstate = {published},
tppubtype = {article}
}