The role of machine learning methods in assessing the risk of neonatal sepsis: A study of biochemical markers and genetic variants.

ABSTRACT: Neonatal sepsis is a life-threatening infection that affects neonates, and its morbidity and mortality rates remain high. There is currently no effective method for timely diagnosis and prevention of neonatal sepsis. Using machine learning techniques, we aimed to analyze the risk factors of neonatal sepsis, including biochemical indicators and genetic variants. We collected data from 107 neonates, 56 of whom were in the sepsis cohort and 51 were not. We classified the data using support vector machine and random forest models and evaluated model performance and feature significance. PCT (Procalcitonin level), WBC (white blood cell count), and IL-6 (interleukin-6 level) were the characteristics most strongly associated with sepsis risk. We analyzed the association between genetic variants i

SUBMITTER: Zhao X 

PROVIDER: S-EPMC12517896 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature