An Investigation into Feature Selection for Oncological Survival Prediction

Authors

Dmitry Strunkin, Kazan State UniversityFollow
Brian Mac Namee, Technological University DublinFollow
John D. Kelleher, Technological University DublinFollow

Document Type

Conference Paper

Rights

Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Disciplines

1.2 COMPUTER AND INFORMATION SCIENCE

Publication Details

Proceedings of the International Conference on Information Technology: New Generations.

Abstract

In machine learning based clinical decision support (CDS) systems the features used to train prediction models are of paramount importance. Strong features will lead to accurate models, whereas as weak features will have the opposite effect. Feature sets can either be designed by domain experts, or automatically extracted for unstructured data that happens to be available from some process other than a CDS system. This paper compares the usefulness of structured expert-designed features to features extracted from unstructured data sources in an oncological survival prediction application scenario.

DOI

https://doi.org/10.1109/ITNG.2012.148

Recommended Citation

Strunkin, D., Mac Namee, B. & Kelleher, J.D. (2012) An Investigation into Feature Selection of Oncological Survival Prediction. Ninth International Conference Information Technology: New Generations (ITNG), Las Vegas, Nevada 16-18 April. doi:10.1109/ITNG.2012.148