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Yu QY, Lin Y, Zhou YR, Yang XJ, Hemelaar J. Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms. Front Big Data 2024; 7:1291196. [PMID: 38495848 PMCID: PMC10941650 DOI: 10.3389/fdata.2024.1291196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 02/12/2024] [Indexed: 03/19/2024] Open
Abstract
We aimed to develop, train, and validate machine learning models for predicting preterm birth (<37 weeks' gestation) in singleton pregnancies at different gestational intervals. Models were developed based on complete data from 22,603 singleton pregnancies from a prospective population-based cohort study that was conducted in 51 midwifery clinics and hospitals in Wenzhou City of China between 2014 and 2016. We applied Catboost, Random Forest, Stacked Model, Deep Neural Networks (DNN), and Support Vector Machine (SVM) algorithms, as well as logistic regression, to conduct feature selection and predictive modeling. Feature selection was implemented based on permutation-based feature importance lists derived from the machine learning models including all features, using a balanced training data set. To develop prediction models, the top 10%, 25%, and 50% most important predictive features were selected. Prediction models were developed with the training data set with 5-fold cross-validation for internal validation. Model performance was assessed using area under the receiver operating curve (AUC) values. The CatBoost-based prediction model after 26 weeks' gestation performed best with an AUC value of 0.70 (0.67, 0.73), accuracy of 0.81, sensitivity of 0.47, and specificity of 0.83. Number of antenatal care visits before 24 weeks' gestation, aspartate aminotransferase level at registration, symphysis fundal height, maternal weight, abdominal circumference, and blood pressure emerged as strong predictors after 26 completed weeks. The application of machine learning on pregnancy surveillance data is a promising approach to predict preterm birth and we identified several modifiable antenatal predictors.
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Affiliation(s)
- Qiu-Yan Yu
- National Perinatal Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Department of Preventive Medicine, School of Public Health, Wenzhou Medical University, Wenzhou, China
| | - Ying Lin
- Wenzhou Women and Children Health Guidance Center, Wenzhou, China
| | - Yu-Run Zhou
- Wenzhou Women and Children Health Guidance Center, Wenzhou, China
| | - Xin-Jun Yang
- Department of Preventive Medicine, School of Public Health, Wenzhou Medical University, Wenzhou, China
| | - Joris Hemelaar
- National Perinatal Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
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Jager F. An open dataset with electrohysterogram records of pregnancies ending in induced and cesarean section delivery. Sci Data 2023; 10:669. [PMID: 37783671 PMCID: PMC10545725 DOI: 10.1038/s41597-023-02581-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 09/20/2023] [Indexed: 10/04/2023] Open
Abstract
The existing non-invasive automated preterm birth prediction methods rely on the use of uterine electrohysterogram (EHG) records coming from spontaneous preterm and term deliveries, and are indifferent to term induced and cesarean section deliveries. In order to enhance current publicly available pool of term EHG records, we developed a new EHG dataset, Induced Cesarean EHG DataSet (ICEHG DS), containing 126 30-minute EHG records, recorded early (23rd week), and/or later (31st week) during pregnancy, of those pregnancies that were expected to end in spontaneous term delivery, but ended in induced or cesarean section delivery. The records were collected at the University Medical Center Ljubljana, Ljubljana, Slovenia. The dataset includes 38 and 43, early and later, induced; 11 and 8, early and later, cesarean; and 13 and 13, early and later, induced and cesarean EHG records. This dataset enables better understanding of the underlying physiological mechanisms involved during pregnancies ending in induced and cesarean deliveries, and provides a robust and more realistic assessment of the performance of automated preterm birth prediction methods.
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Affiliation(s)
- Franc Jager
- Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000, Ljubljana, Slovenia.
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S H, V MA. An idiosyncratic MIMBO-NBRF based automated system for child birth mode prediction. Artif Intell Med 2023; 143:102621. [PMID: 37673564 DOI: 10.1016/j.artmed.2023.102621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 05/11/2023] [Accepted: 07/01/2023] [Indexed: 09/08/2023]
Abstract
Predicting the mode of child birth is still remains one of the most complex and challenging tasks in ancient times. Also, there is no such strong methodologies are developed in the conventional works for birth mode prediction. Therefore, the proposed work objects to develop a novel and distinct optimization based machine learning technique for creating the child birth mode prediction system. This framework includes the modules of data imputation, feature selection, classification, and prediction. Initially, the data imputation process is performed to improve the quality of dataset by normalizing the attributes and filling the missed fields. Then, the Multivariate Intensified Mine Blast Optimization (MIMBO) technique is implemented to choose the best set of features by estimating the optimal function. After that, an integrated Naïve Bayes - Random Forest (NBRF) technique is developed by incorporating the functions of conventional NB and RF techniques. The novel contribution of this technique, a Bird Mating (BM) optimization technique is used in NBRF classifier for estimating the likelihood parameter to generate the Bayesian rules. The main idea of this paper is to develop a simple as well as efficient automated system with the use of hybrid machine learning model for predicting the mode of child birth. For this purpose, advanced algorithms such as MIMBO based feature selection, and NBRF based classification are implemented in this work. Due to the inclusion of MIMBO and BM optimization techniques, the performance of classifier is greatly improved with low computational burden and increased prediction accuracy. Moreover, the combination of proposed MIMBO-NBRF technique outperforms the existing child birth prediction methods with superior results in terms of average accuracy up to 99 %. In addition, some other parameters are also estimated and compared with the existing techniques for proving the overall superiority of the proposed framework.
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Affiliation(s)
- Hemalatha S
- Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai 600 119, Tamilnadu, India.
| | - Maria Anu V
- Department of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamilnadu, India
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Shimoga Narayana Rao K, Asha V. An automatic classification approach for preterm delivery detection based on deep learning. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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Mohammadi Far S, Beiramvand M, Shahbakhti M, Augustyniak P. Prediction of Preterm Labor from the Electrohysterogram Signals Based on Different Gestational Weeks. SENSORS (BASEL, SWITZERLAND) 2023; 23:5965. [PMID: 37447815 DOI: 10.3390/s23135965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 06/15/2023] [Accepted: 06/22/2023] [Indexed: 07/15/2023]
Abstract
Timely preterm labor prediction plays an important role for increasing the chance of neonate survival, the mother's mental health, and reducing financial burdens imposed on the family. The objective of this study is to propose a method for the reliable prediction of preterm labor from the electrohysterogram (EHG) signals based on different pregnancy weeks. In this paper, EHG signals recorded from 300 subjects were split into 2 groups: (I) those with preterm and term labor EHG data that were recorded prior to the 26th week of pregnancy (referred to as the PE-TE group), and (II) those with preterm and term labor EHG data that were recorded after the 26th week of pregnancy (referred to as the PL-TL group). After decomposing each EHG signal into four intrinsic mode functions (IMFs) by empirical mode decomposition (EMD), several linear and nonlinear features were extracted. Then, a self-adaptive synthetic over-sampling method was used to balance the feature vector for each group. Finally, a feature selection method was performed and the prominent ones were fed to different classifiers for discriminating between term and preterm labor. For both groups, the AdaBoost classifier achieved the best results with a mean accuracy, sensitivity, specificity, and area under the curve (AUC) of 95%, 92%, 97%, and 0.99 for the PE-TE group and a mean accuracy, sensitivity, specificity, and AUC of 93%, 90%, 94%, and 0.98 for the PL-TL group. The similarity between the obtained results indicates the feasibility of the proposed method for the prediction of preterm labor based on different pregnancy weeks.
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Affiliation(s)
| | - Matin Beiramvand
- Faculty of Information Technology and Communication, Tampere University, 33100 Tampere, Finland
| | - Mohammad Shahbakhti
- Biomedical Engineering Institute, Kaunas University of Technology, 51423 Kaunas, Lithuania
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Diaz-Martinez A, Monfort-Ortiz R, Ye-Lin Y, Garcia-Casado J, Nieto-Tous M, Nieto-Del-Amor F, Diago-Almela V, Prats-Boluda G. Uterine myoelectrical activity as biomarker of successful induction with Dinoprostone: Influence of parity. Biocybern Biomed Eng 2023. [DOI: 10.1016/j.bbe.2022.12.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Pirnar Ž, Jager F, Geršak K. Characterization and separation of preterm and term spontaneous, induced, and cesarean EHG records. Comput Biol Med 2022; 151:106238. [PMID: 36343404 DOI: 10.1016/j.compbiomed.2022.106238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 09/30/2022] [Accepted: 10/22/2022] [Indexed: 12/27/2022]
Abstract
To improve the understanding of the underlying physiological processes that lead to preterm birth, and different term delivery modes, we quantitatively characterized and assessed the separability of the sets of early (23rd week) and later (31st week) recorded, preterm and term spontaneous, induced, cesarean, and induced-cesarean electrohysterogram (EHG) records using several of the most widely used non-linear features extracted from the EHG signals. Linearly modeled temporal trends of the means of the median frequencies (MFs), and of the means of the peak amplitudes (PAs) of the normalized power spectra of the EHG signals, along pregnancy (from early to later recorded records), derived from a variety of frequency bands, revealed that for the preterm group of records, in comparison to all other term delivery groups, the frequency spectrum of the frequency band B0L (0.08-0.3 Hz) shifts toward higher frequencies, and that the spectrum of the newly identified frequency band B0L' (0.125-0.575 Hz), which approximately matches the Fast Wave Low band, becomes stronger. The most promising features to separate between the later preterm group and all other later term delivery groups appear to be MF (p=1.1⋅10-5) in the band B0L of the horizontal signal S3, and PA (p=2.4⋅10-8) in the band B0L' (S3). Moreover, the PA in the band B0L' (S3) showed the highest power to individually separate between the later preterm group and any other later term delivery group. Furthermore, the results suggest that in preterm pregnancies the resting maternal heart rate decreases between the 23rd and 31st week of gestation.
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Affiliation(s)
- Žiga Pirnar
- Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia
| | - Franc Jager
- Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia.
| | - Ksenija Geršak
- Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia; University Medical Center Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia
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