Conclusions: Machine learning models show great promise for improving preterm birth prediction, enabling earlier and more accurate interventions. These models outperform traditional methods and offer a valuable tool for reducing the global burden of preterm birth and enhancing healthcare delivery.
Sep 12, 2024
In this study, the possibility of an early prediction of preterm delivery from the EHG recordings made between 22 nd and 25 th week of the gestation is explored�...
Among fetus or newborn populations, non-LR algorithms were mostly applied to develop predictions for outcome categories of premature birth (12/50, 24%) [111,112�...
Abstract—The preterm birth presents a major cause of the infants' deaths, or the consequent health impairments globally, with an increasing trend of the�...
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This study aimed to systematically review the literature concerned with PTB prediction using HR data and the ML approach.
Nine machine learning methods were applied and compared for the prediction of preterm birth and the support vector machine showed the best performance in�...
Apr 13, 2022 � The aim of this study is to propose a prediction model based on machine learning algorithms for PTB.
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Nov 10, 2023 � The purpose of this study was to construct a preterm birth prediction model based on electronic health records and to provide a reference for preterm birth�...
Nov 30, 2023 � A preterm birth prediction model was developed using machine learning of oral microbiome compositions in mouthwash samples.
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Sep 12, 2024 � Prediction was not improved when using machine learning approaches over traditional statistical learning. These findings underscore the�...