Publications

@article{cueto-lopez_assessing_2018,

title = {Assessing feature selection techniques for a colorectal cancer prediction model},

author = {Nahúm Cueto-López and Rocío Alaiz-Rodríguez and María Teresa García-Ordás and Carmen González-Donquiles and Vicente Martín},

url = {https://link.springer.com/chapter/10.1007/978-3-319-67180-2_46},

year  = {2018},

date = {2018-01-01},

journal = {International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding 12},

pages = {471–481},

abstract = {Risk prediction models for colorectal cancer help identify high-risk individuals and key risk factors. This study evaluates feature ranking algorithms in terms of stability and performance. Results show that Random Forest (RF) is the most stable but not the best-performing model, while SVM-wrapper and Pearson correlation achieve a balance between stability and predictive accuracy.},

note = {Publisher: Springer International Publishing},

keywords = {colorectal cancer, feature selection, healthcare analytics, machine learning, risk prediction},

pubstate = {published},

tppubtype = {article}

}