Machine Learning to Predict Pre-Eclampsia and Intrauterine Growth Restriction in Pregnant Women

The use of artificial intelligence in healthcare in general and in obstetrics and gynecology in particular has great potential. Specifically, machine learning methods could help improve the health and well-being of pregnant women, closely monitoring their health parameters during pregnancy, or reduc...

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Published in:Electronics (Basel) 2022-10, Vol.11 (19), p.3240
Main Authors: Gómez-Jemes, Lola, Oprescu, Andreea Madalina, Chimenea-Toscano, Ángel, García-Díaz, Lutgardo, Romero-Ternero, María del Carmen
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Biomarkers
Classification
Classifiers
Datasets
Decision trees
Diagnosis
Fetuses
Gestational age
Gestational diabetes
Gynecology
Health aspects
Health care facilities
Hypertension
Machine learning
Mathematical models
Mortality
Parameters
Preeclampsia
Pregnancy
Pregnancy, Complications of
Pregnant women
Recall
Ultrasonic imaging
Variables
Veins & arteries
Womens health
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Summary:The use of artificial intelligence in healthcare in general and in obstetrics and gynecology in particular has great potential. Specifically, machine learning methods could help improve the health and well-being of pregnant women, closely monitoring their health parameters during pregnancy, or reducing maternal and perinatal morbidity and mortality with early detection of pathologies. In this work, we propose a machine learning model to predict risk events in pregnancy, in particular the prediction of pre-eclampsia and intrauterine growth restriction, using Doppler measures of the uterine artery, sFlt-1, and PlGF values. For this purpose, we used a public dataset from a study carried out by the University Medical Center of Ljubljana, in which data were collected from 95 pregnant women with pre-eclampsia and intrauterine growth restriction. We adopted a multi-label approach to accomplish the prediction task. Different classifiers were evaluated and compared. The performance of each model was tested in terms of accuracy, precision, recall, F1 score, Hamming loss, and AUC-ROC. On the basis of these parameters, a variation of the decision tree classifier was found to be the best performing model. Our model had a robust recall metric (0.89) and an AUC ROC metric (0.87), taking into account the size of the data and the unbalance of the class.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11193240