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Learning Bayesian Network to Predict Group Emotion in Kindergarten by Evolutionary Computation Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Assessing Feature Selection Techniques for a Colorectal Cancer Prediction Model

Authors : Nahúm Cueto-López, Rocío Alaiz-Rodríguez, María Teresa García-Ordás, Carmen González-Donquiles, Vicente Martín

Published in: International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding

Publisher: Springer International Publishing

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Abstract

Risk prediction models for colorectal cancer play an important role to identify people at higher risk of developing this disease as well as the risk factors associated with it. Feature selection techniques help to improve the prediction model performance and to gain insight in the data itself. The assessment of the stability of feature selection/ranking algorithms becomes an important issue when the aim is to analyze the most relevant features. This work assesses several feature ranking algorithms in terms of performance and robustness for a set of risk prediction models. Experimental results demonstrate that stability and model performance should be studied jointly as RF turned out to be the most stable algorithm but outperformed by others in terms of model performance while SVM-wrapper and the Pearson correlation coefficient are moderately stable while achieving good model performance.

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previous chapter A Machine Learning Based System for Analgesic Drug Delivery
next chapter Data Mining Techniques for the Estimation of Variables in Health-Related Noisy Data
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Metadata
Title
Assessing Feature Selection Techniques for a Colorectal Cancer Prediction Model
Authors
Nahúm Cueto-López
Rocío Alaiz-Rodríguez
María Teresa García-Ordás
Carmen González-Donquiles
Vicente Martín
Copyright Year
2018
Book
International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding

Print ISBN: 978-3-319-67179-6

Electronic ISBN: 978-3-319-67180-2

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-67180-2_46

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