Predicting preterm birth through vaginal microbiota, cervical length and WBC using a machine learning model

The purpose of this study was to select markers that improve predictive power through machine learning among various vaginal microbiota and develop an excellent prediction algorithm that combines clinical information. As a multicenter case-control study with 150 Korean pregnant women, cervicovaginal fluid were collected from pregnant women during mid-pregnancy. Their demographic profiles, white blood cell count, and cervical length were recorded, and the microbiome profiles of the cervicovaginal fluid were analyzed.