ArticleHealth information science and systems2019
Prediction of intrapartum fetal hypoxia considering feature selection algorithms and machine learning models.
Article in Health information science and systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Non-linear Methods Predominant in Fetal Heart Rate Analysis: A Systematic Review.Frontiers in medicine · 2021Pooled it
- Machine learning based classification in obstetrics: evaluating models, partitioning strategies, and key predictors in cardiotocography.BMC pregnancy and childbirth · 2026Article
- Comparison of Feature Selection Methods in Machine Learning Models of Cancer Information Seeking Among United States Adults: Cross-Sectional Study.JMIR medical informatics · 2026Article
- Enhancing cardiotocography classification via ensemble learning and threshold optimization.Scientific reports · 2025Article
- Review
- Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers.International journal of molecular sciences · 2025Article
- Artificial intelligence-large language models (AI-LLMs) for reliable and accurate cardiotocography (CTG) interpretation in obstetric practice.Computational and structural biotechnology journal · 2025Article
- Comparison and verification of detection accuracy for late deceleration with and without uterine contractions signals using convolutional neural networks.Frontiers in physiology · 2025Article
- Feature Selection and Machine Learning Approaches in Prediction of Current E-Cigarette Use Among U.S. Adults in 2022.International journal of environmental research and public health · 2024Article
- Machine Learning-Based Prediction of Binge Drinking among Adults in the United State: Analysis of the 2022 Health Information National Trends Survey.Proceedings of the 2024 9th International Conference on Mathematics and Artificial Intelligence · 2024Article
- Intelligent antepartum fetal monitoring via deep learning and fusion of cardiotocographic signals and clinical data.Health information science and systems · 2023Article
- Fetal Health State Detection Using Interval Type-2 Fuzzy Neural Networks.Diagnostics (Basel, Switzerland) · 2023Article
- Fetal Health Classification from Cardiotocograph for Both Stages of Labor-A Soft-Computing-Based Approach.Diagnostics (Basel, Switzerland) · 2023Article
- Machine learning and disease prediction in obstetrics.Current research in physiology · 2023Review
- Accessing Artificial Intelligence for Fetus Health Status Using Hybrid Deep Learning Algorithm (AlexNet-SVM) on Cardiotocographic Data.Sensors (Basel, Switzerland) · 2022Article
- Heart rate markers for prediction of fetal acidosis in an experimental study on fetal sheep.Scientific reports · 2022Article
- Intrapartum cardiotocography trace pattern pre-processing, features extraction and fetal health condition diagnoses based on RCOG guideline.PeerJ. Computer science · 2022Article
- Cardiotocograph-based labor stage classification from uterine contraction pressure during ante-partum and intra-partum period: a fuzzy theoretic approach.Health information science and systems · 2020Article
- Personalized Body Constitution Inquiry Based on Machine Learning.Journal of healthcare engineering · 2020Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionCardiotocography (CTG) consists of two biophysical signals that are fetal heart rate (FHR) and uterine contraction (UC). In this research area, the computerized systems are usually utilized to provide more objective and repeatable results. MATERIALS AND
methodsFeature selection algorithms are of great importance regarding the computerized systems to not only reduce the dimension of feature set but also to reveal the most relevant features without losing too much information. In this paper, three filters and two wrappers feature selection methods and machine learning models, which are artificial neural network (ANN),
resultsThe experimental results show that it is possible to achieve better classification performance using lower dimensional feature set that comprises of more related features, instead of the high-dimensional feature set. The most informative feature subset was generated by considering the frequency of selection of the features by feature selection algorithms. As a result, the most efficient results were produced by selected only 12 relevant features instead of a full feature set consisting of 30 diagnostic indices and SVM model. Sensitivity and specificity were achieved as 77.40% and 93.86%, respectively.
conclusionConsequently, the evaluation of multiple feature selection algorithms resulted in achieving the best results.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.