Kidney transplant classification with gene expression profiles using L1 feature selection ensemble classifier based on data clustering

Gene expression profiles can be extracted from DNA in order to obtain relevant information related to kidney transplant. Successful kidney transplant from donor to patient depends on the fitness of both kidneys, so more and more study should be conducted particularly in kidney transplant classification. The common problem of kidney transplant classification is large amount of genes data from various samples. In this research, we demonstrate L1 Feature Selection Ensemble Classifier based on Data Clustering to select informative genes in order to classify gene expression profiles. After classification on data clustering, ensemble classifier produces 97% overall accuracy with precision, recall, F-Test and Kappa Coefficient reaches 95.7%, 91.3%, 93.5%, 90.3% respectively.