Complete blood count as a biomarker for preeclampsia with severe features diagnosis: a machine learning approach.

ABSTRACT:

Objective

This study introduces the complete blood count (CBC), a standard prenatal screening test, as a biomarker for diagnosing preeclampsia with severe features (sPE), employing machine learning models.

Methods

We used a boosting machine learning model fed with synthetic data generated through a new methodology called DAS (Data Augmentation and Smoothing). Using data from a Brazilian study including 132 pregnant women, we generated 3,552 synthetic samples for model training. To improve interpretability, we also provided a ridge regression model.

Results

Our boosting model obtained an AUROC of 0.90±0.10, sensitivity of 0.95, and specificity of 0.79 to differentiate sPE and non-PE pregnant women, using CBC parameters of neutrophils count, mean corpuscular hemoglobin

SUBMITTER: Araujo DC 

PROVIDER: S-EPMC11445858 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature