A comparative study on feature selection for a risk prediction model for
  colorectal cancer
N. Cueto-López M. T. García-Ordás V. Dávila-Batista V. Moreno N. Aragonés R. Alaiz-Rodríguez
Background and objective
  Risk prediction models aim at identifying people at higher risk of developing
a target disease. Feature selection is particularly important to improve the
prediction model performance avoiding overfitting and to identify the leading
cancer risk (and protective) factors. Assessing the stability of feature
selection/ranking algorithms becomes an important issue when the aim is to
analyze the features with more prediction power. Methods
  This work is focused on colorectal cancer, assessing several feature ranking
algorithms in terms of performance for a set of risk prediction models (Neural
Networks, Support Vector Machines (SVM), Logistic Regression, k-Nearest
Neighbors and Boosted Trees). Additionally, their robustness is evaluated
following a conventional approach with scalar stability metrics and a visual
approach proposed in this work to study both similarity among feature ranking
techniques as well as their individual stability. A comparative analysis is
carried out between the most relevant features found out in this study and
features provided by the experts according to the state-of-the-art knowledge.
Results
  The two best performance results in terms of Area Under the ROC Curve (AUC)
are achieved with a SVM classifier using the top-41 features selected by the
SVM wrapper approach (AUC=0.693) and Logistic Regression with the top-40
features selected by the Pearson (AUC=0.689). Experiments showed that
performing feature selection contributes to classification performance with a
3.9% and 1.9% improvement in AUC for the SVM and Logistic Regression
classifier, respectively, with respect to the results using the full feature
set. The visual approach proposed in this work allows to see that the Neural
Network-based wrapper ranking is the most unstable while the Random Forest is
the most stable.