Automatic Feature Construction Based on Genetic Programming for Survival Prediction in Lung Cancer Using CT Images

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Scalco:2022:EMBC,

• author = "Elisa Scalco and Giovanna Rizzo and Wilfrido Gomez-Flores",
• title = "Automatic Feature Construction Based on Genetic Programming for Survival Prediction in Lung Cancer Using {CT} Images",
• booktitle = "2022 44th Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC)",
• year = "2022",
• pages = "3797--3800",
• abstract = "In the radiomics workflow, machine learning builds classification models from a set of input features. However, some features can be irrelevant and redundant, reducing the classification performance. This paper proposes using the Genetic Programming (GP) algorithm to automatically construct a reduced number of independent and relevant radiomic features. The proposed method is applied to patients affected by Non-Small Cell Lung Cancer (NSCLC) with pre-operative computed tomography (CT) images to predict the two-year survival by the use of linear classifiers. The model built using GP features is compared with benchmark models built using traditional features. The use of the GP algorithm increased classification performance: AUC =0.69 for the proposed model vs. AUC =0.66 and 0.64 for the benchmark models. Hence, the proposed approach better stratifies patients at high and low risk according to their overall postoperative survival time.",
• keywords = "genetic algorithms, genetic programming, Machine learning algorithms, Computed tomography, Computational modeling, Biological system modeling, Lung cancer, Benchmark testing",
• DOI = "doi:10.1109/EMBC48229.2022.9871039",
• ISSN = "2694-0604",
• month = jul,
• notes = "Also known as \cite{9871039}",

}

Genetic Programming entries for Elisa Scalco Giovanna Rizzo Wilfrido Gomez-Flores

Citations