An efficient feature selection paradigm using PCA-CFS-Shapley values ensemble applied to small medical data sets

The precise diagnosis of patient profiles into categories, such as presence or absence of a particular disease along with its level of severity, remains to be a crucial challenge in biomedical field. This process is realized by the performance of the classifier by using a supervised training set with labeled samples. Then based on the result obtained, the classifier is allowed to predict the labels of new samples. Due to presence of irrelevant features it is difficult for standard classifiers from obtaining good detection rates. Hence it is important to select the features which are more relevant and by with good classifiers could be constructed to obtain a good accuracy and efficiency. This study is aimed to classify the medical profiles, and is realized by feature extraction (FE), feature ranking (FR) and dimension reduction methods (Shapley Values Analysis) as a hybrid procedure to improve the classification efficiency and accuracy. To appraise the success of the proposed method, experiments were conducted across 6 different medical data sets using J48 decision tree classifier. The experimental results showed that using the PCA-CFS-Shapley Values analysis procedure improves the classification efficiency and accuracy compared with individual usage.