A Study on Machine Learning Based Gene Selection for Pediatric Sepsis Classification

Nowadays, sepsis has been known as a serious health problem which becomes one of the major causes of mortality even in developed countries’ ICUs. The use of gene expression analysis and machine learning is an attractive and excellent solution to diagnose accurately the disease severity. Unfortunately, the collected dataset contains many genes among which highly correlated genes exist (redundant genes), which reduces the performance of diagnostics. Hence, efficient gene selection methods are necessary but challenging. In this paper, in order to verify the advantages of the machine learning technique and the gene expression analysis in pediatric sepsis classification, we evaluate and compare the performance of different gene selection methods combined with distinct machine learning models that are Filter, Wrapper, and Embedded approaches. In our work, a sequential gene selection procedure incorporates the differential gene expression analysis with the gene importance determined by the applied machine learning model to figure out the most informative differential gene expression. We also employ the cross-validation procedure in combination with different machine learning algorithms to verify the diagnosis performance. Numerical experiments demonstrate that the Embedded-based gene selection method incorporated with various machine learning models offers the highest diagnostic performance. In particular, the random forest-based gene selection algorithm combined with the SVM (Support Vector Machine) model that identifies a combination of 4 genes attains the best performance. The validation results have an accuracy of 89.6%, a sensitivity of 41.5%, and a specificity of 97.5%, showing the applicability of this approach in the clinic environment.