  Machine learning methods with quantitative imaging features integration have
recently gained a lot of attention for lung nodule classification. However,
there is a dearth of studies in the literature on effective features ranking
methods for classification purpose. Moreover, optimal number of features
required for the classification task also needs to be evaluated. In this study,
we investigate the impact of supervised and unsupervised feature selection
techniques on machine learning methods for nodule classification in Computed
Tomography (CT) images. The research work explores the classification
performance of Naive Bayes and Support Vector Machine(SVM) when trained with 2,
4, 8, 12, 16 and 20 highly ranked features from supervised and unsupervised
ranking approaches. The best classification results were achieved using SVM
trained with 8 radiomic features selected from supervised feature ranking
methods and the accuracy was 100%. The study further revealed that very good
nodule classification can be achieved by training any of the SVM or Naive Bayes
with a fewer radiomic features. A periodic increment in the number of radiomic
features from 2 to 20 did not improve the classification results whether the
selection was made using supervised or unsupervised ranking approaches.
