Pediatric Sepsis Diagnosis Based on Differential Gene Expression and Machine Learning Method

Abstract

Sepsis is known as a life-threading status, which relates closely to the responses of the human body to an infection inside the tissues and organs. Such a reaction results in the distortion of the organ function. In this work, a novel algorithm is proposed for the diagnosis of pediatric sepsis including a random forest model and a combination of 9 genes. The proposed algorithm is constructed carefully with a sequential gene selection procedure, which combines differential gene expression analysis and gene importance computed by the machine learning model to address the most informative differential gene expression. The cross-validation procedure in combination with different machine learning algorithms is adopted for the estimation of the diagnosis performance related to the gene combinations and machine learning models. The selected gene combinations are then tested separately using various machine learning methods. The validation results, which are accuracy of 91.79%, sensitivity of 57.33%, and specificity of 100%, show that the proposed algorithm is potential for practical application in the real clinic environment.