Screening features to improve the class prediction of acute myeloid leukemia and myelodysplastic syndrome

After more than three decades of intensive investigations, the underpinning mechanism of myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) pathogenesis still remains largely uncharacterized, and their diagnosis relies heavily on the subjective factors. Recently gene expression profiling technique showed significant improvement in classifying some subtypes of AML, but the model's discriminating power of MDS from AML is still in its infancy. Feature selection plays an important role in the classification of the samples on the basis of the gene expression profiles. Our hypothesis explains that a better choice of features could improve the classification of the diseased and normal stage samples, and the potential application of feature screening to produce feature sets, with better accuracies and lowest number of embedded features. The observed results suggest that feature selection proves to be an essential and affirmative step in the biomedical data mining models based on gene expression profiles.

► We studied gene expression profile based classification of AML and MDS. ► Local greedy hill-climbing method detects better features to classify AML and MDS. ► Combination of multiple feature selection strategies works better. ► Better feature selection strategy produces higher accuracy and fewer features. ► Bayes net based classifier outperforms the other algorithms in most cases.