Combination of Feature Selection and Resampling Methods to Predict Preterm Birth Based on Electrohysterographic Signals from Imbalance Data

Abstract

Due to its high sensitivity, electrohysterography (EHG) has emerged as an alternative technique for predicting preterm labor. The main obstacle in designing preterm labor prediction models is the inherent preterm/term imbalance ratio, which can give rise to relatively low performance. Numerous studies obtained promising preterm labor prediction results using the synthetic minority oversampling technique. However, these studies generally overestimate mathematical models’ real generalization capacity by generating synthetic data before splitting the dataset, leaking information between the training and testing partitions and thus reducing the complexity of the classification task. In this work, we analyzed the effect of combining feature selection and resampling methods to overcome the class imbalance problem for predicting preterm labor by EHG. We assessed undersampling, oversampling, and hybrid methods applied to the training and validation dataset during feature selection by genetic algorithm, and analyzed the resampling effect on training data after obtaining the optimized feature subset. The best strategy consisted of undersampling the majority class of the validation dataset to 1:1 during feature selection, without subsequent resampling of the training data, achieving an AUC of 94.5 ± 4.6%, average precision of 84.5 ± 11.7%, maximum F1-score of 79.6 ± 13.8%, and recall of 89.8 ± 12.1%. Our results outperformed the techniques currently used in clinical practice, suggesting the EHG could be used to predict preterm labor in clinics.

Machine learning and disease prediction in obstetrics

“…This appeared more robust than earlier methods to automate uterine contraction detection which showed falls in sensitivity and PPV on low quality recordings ( Horoba et al, 2016 ). Recently, algorithms from electrohysterography signals, acquired by electrodes placed on the mother's abdomen are being developed to predict embryo implantation for IVF patients ( Sammali et al, 2021 ) and preterm birth ( Cheng et al, 2022 ; Prats-Boluda et al, 2021 , Nieto-del-Amor et al, 2022 ). One method using novel multichannel entropy features appeared accurate in predicting preterm birth (90.5%) ( Cheng et al, 2022 ).…”

Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms

“…Fourth, we did not have information on the length of the uterine cervix, which is a known predictor of PTB. Fifth, although we used hybrid and under-sampling methods in the training data set to improve model performance, we did not balance the validation and testing sets to assess model performance, as some previous studies did (Nieto-Del-Amor et al, 2022;Kyparissidis Kokkinidis et al, 2023). Finally, there may have been misclassification and selection bias in our electronic health record-based study.…”

“…With imbalanced data, prediction models tend to favor the majority class as outcome to achieve high accuracy. Thus, we applied the over/undersampling hybrid method and the K-nearest neighbors for the undersampling method to balance the training data set, while keeping the testing and validation data sets imbalanced (Zhang et al, 2010;Nieto-Del-Amor et al, 2022). In our study, with the resampling methods, the training data set was resamplied into balanced data, resulting in preterm births and term births each occupying 50%, respectively.…”

“…Prediction models using ML algorithms to quantify the risk of PTB have been proposed in recent years, with predictive powers ranging from 0.6 to 0.9 (Weber et al, 2018;Koivu and Sairanen, 2020;Arabi Belaghi et al, 2021;Raja et al, 2021;Shields et al, 2021;Lee et al, 2022;Nieto-Del-Amor et al, 2022;Sun et al, 2022). Some ML prediction models using uterine electrohysterographic (EHG) signals and multi-omics in the middle trimester reported a good ability to differentiate between preterm and term birth (Tarca et al, 2021;Mohammadi Far et al, 2022;Nieto-Del-Amor et al, 2022;Romero-Morales et al, 2022;Espinosa et al, 2023). However, these predictors are time consuming and costly, and are impossible to measure in routine antenatal care in low-resource settings.…”

An open dataset with electrohysterogram records of pregnancies ending in induced and cesarean section delivery

“…Merging EHG records from different databases/dataset may be questionable due to the differences in signal acquisition protocols. Since the EHG records of the TPEGH DB and TPEHGT DS were acquired under the same acquisition protocol, and using the same recording device, several authors merged the EHG records from these two database/dataset in cases of traditional feature engineering 24 , 25 , 32 , 33 and deep learning approach 37 .…”