On the prediction of premature births in Hispanic labour patients using uterine contractions, heart beat signals and prediction machines

Abstract

Preterm birth is a global epidemic affecting millions of mothers across different ethnicities. The cause of the condition remains unknown but has recognised health‐based implications, in addition to financial and economic ones. Machine Learning methods have enabled researchers to combine datasets using uterine contraction signals with various forms of prediction machines to improve awareness of the likelihood of premature births. This work investigates the feasibility of enhancing these prediction methods using physiological signals including uterine contractions, and foetal and maternal heart rate signals, for a population of south American women in active labour. As part of this work, the use of the Linear Series Decomposition Learner (LSDL) was seen to lead to an improvement in the prediction accuracies of all models, which included supervised and unsupervised learning models. The results from the supervised learning models showed high prediction metrics upon the physiological signals being pre‐processed by the LSDL for all variations of the physiological signals. The unsupervised learning models showed good metrics for the partitioning of Preterm/Term labour patients from their uterine contraction signals but produced a comparatively lower set of results for the various kinds of heart rate signals investigated.

Image Formation Algorithms for Low-Cost Freehand Ultrasound Scanner Based on Ego-Motion Estimation and Unsupervised Clustering

“…This study is based on predicting premature births in Hispanic labour patients. The results have shown the use of two effective unsupervised learning methods, that is, GMM and K-means, towards the self-sorting of data samples based on the acquired physiological signals [55].…”

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

“…Instead, prediction models using maternal features available from routine pregnancy care are more likely to be widely applicable and improve pregnancy outcomes. To improve predictive power of PTB, many popular ML algorithms have been employed and compared with traditional regression methods and achieved high areas under the receiver operating characteristic curve (AUC) (Fazzari et al, 2022;Park et al, 2022;Nsugbe et al, 2023). A number of studies found that logistic regression provided quicker and better classification performance, and easier interpretability than ML models in other disease settings (Kuhle et al, 2018;Song et al, 2023).…”

“…To achieve an efficient prediction model, feature selection is an important process to reduce dimensionality and computing complexity, and facilitate clinical practice. There are two conventional ways to conduct feature selection: one is applying univariate analysis to select features which are highly associated with the outcome (Park et al, 2022;Nsugbe et al, 2023), another is relying on feature importance derived from ML algorithms (Sharifi-Heris et al, 2022;Espinosa et al, 2023). However, some known important features might be ignored when only relying on ML-based feature importance lists (Bose et al, 2019;Liverani et al, 2023).…”