Intrapartum fever prediction for pregnant woman from microbial data

The usage of deep learning in microbial data has come a long way since its early days. For fever detection during pregnancy, this paper collects microorganism data from vaginal microbes and trains machine-learning models to classify their class, taxa, phylum, and distribution. This paper presents a novel approach to predicting the etiology of intrapartum fever in pregnant women using machine learning classifiers on vaginal microbial data. High-throughput sequencing was used to analyze a diverse dataset of microbiome abundance profiles. Various classification algorithms were employed to develop predictive models, including logistic regression, support vector machines, and random forests. The study also aimed to identify key microbial taxa or functional profiles associated with each etiology. The XGBoost algorithm emerged as the most accurate, with an accuracy rate of 86%. The study underscores the significance of features such as chills and delivery mode in predicting intrapartum fever. The findings suggest that machine learning can enhance diagnostic accuracy and guide interventions, with potential implications for improving clinical decision-making and care for pregnant women and neonates. Further research is needed to validate these findings and explore broader machine learning applications in reproductive health.