Publications

@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}

}