Integrated Deep Learning and Supervised Machine Learning Model for Predictive Fetal Monitoring
Asphyxiation associated with metabolic acidosis is one of the common causes of fetal deaths. The paper aims to develop a feature extraction and prediction algorithm capable of identifying most of the features in the SISPORTO software package and late and variable decelerations. The resulting feature...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/12/11/2843 |
| _version_ | 1797465592370823168 |
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| author | Vinayaka Gude Steven Corns |
| author_facet | Vinayaka Gude Steven Corns |
| author_sort | Vinayaka Gude |
| collection | DOAJ |
| description | Asphyxiation associated with metabolic acidosis is one of the common causes of fetal deaths. The paper aims to develop a feature extraction and prediction algorithm capable of identifying most of the features in the SISPORTO software package and late and variable decelerations. The resulting features were used for classification based on umbilical cord pH data. The algorithms developed here were used to predict cord pH levels. The prediction system assists the obstetricians in assessing the state of the fetus better than the category methods, as only about 30% of the patients in the pathological category suffer from acidosis, while the majority of acidotic babies were in the suspect category, which is considered lower risk. By predicting the direct indicator of acidosis, umbilical cord pH, this work demonstrates a methodology, which uses fetal heart rate and uterine activity, to identify acidosis. This paper introduces a forecasting model based on deep learning to predict heart rate and uterine contractions, integrated with the classification algorithm, resulting in a robust tool for predictive fetal monitoring. The hybrid algorithm resulted in a model capable of providing future conditions of the fetus, which obstetricians can use for diagnosis and planning interventions. The ensemble classification algorithm had a test accuracy of 85% (<i>n</i> = 24) in predicting fetal acidosis on the features extracted from the cardiotocography data. When integrated with the classification model, the results from the prediction model (long short-term memory network) can effectively identify fetal acidosis 2 or 4 min in the future. |
| first_indexed | 2024-03-09T18:23:43Z |
| format | Article |
| id | doaj.art-b570752d98fe47119e5570c9c1f7b21c |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T18:23:43Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-b570752d98fe47119e5570c9c1f7b21c2023-11-24T08:05:09ZengMDPI AGDiagnostics2075-44182022-11-011211284310.3390/diagnostics12112843Integrated Deep Learning and Supervised Machine Learning Model for Predictive Fetal MonitoringVinayaka Gude0Steven Corns1Department of Marketing and Business Analytics, Texas A&M University—Commerce, Commerce, TX 75428, USADepartment of Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USAAsphyxiation associated with metabolic acidosis is one of the common causes of fetal deaths. The paper aims to develop a feature extraction and prediction algorithm capable of identifying most of the features in the SISPORTO software package and late and variable decelerations. The resulting features were used for classification based on umbilical cord pH data. The algorithms developed here were used to predict cord pH levels. The prediction system assists the obstetricians in assessing the state of the fetus better than the category methods, as only about 30% of the patients in the pathological category suffer from acidosis, while the majority of acidotic babies were in the suspect category, which is considered lower risk. By predicting the direct indicator of acidosis, umbilical cord pH, this work demonstrates a methodology, which uses fetal heart rate and uterine activity, to identify acidosis. This paper introduces a forecasting model based on deep learning to predict heart rate and uterine contractions, integrated with the classification algorithm, resulting in a robust tool for predictive fetal monitoring. The hybrid algorithm resulted in a model capable of providing future conditions of the fetus, which obstetricians can use for diagnosis and planning interventions. The ensemble classification algorithm had a test accuracy of 85% (<i>n</i> = 24) in predicting fetal acidosis on the features extracted from the cardiotocography data. When integrated with the classification model, the results from the prediction model (long short-term memory network) can effectively identify fetal acidosis 2 or 4 min in the future.https://www.mdpi.com/2075-4418/12/11/2843cardiotocographyacidosissupport vector machinesrandom forestsmachine learningoversampling |
| spellingShingle | Vinayaka Gude Steven Corns Integrated Deep Learning and Supervised Machine Learning Model for Predictive Fetal Monitoring Diagnostics cardiotocography acidosis support vector machines random forests machine learning oversampling |
| title | Integrated Deep Learning and Supervised Machine Learning Model for Predictive Fetal Monitoring |
| title_full | Integrated Deep Learning and Supervised Machine Learning Model for Predictive Fetal Monitoring |
| title_fullStr | Integrated Deep Learning and Supervised Machine Learning Model for Predictive Fetal Monitoring |
| title_full_unstemmed | Integrated Deep Learning and Supervised Machine Learning Model for Predictive Fetal Monitoring |
| title_short | Integrated Deep Learning and Supervised Machine Learning Model for Predictive Fetal Monitoring |
| title_sort | integrated deep learning and supervised machine learning model for predictive fetal monitoring |
| topic | cardiotocography acidosis support vector machines random forests machine learning oversampling |
| url | https://www.mdpi.com/2075-4418/12/11/2843 |
| work_keys_str_mv | AT vinayakagude integrateddeeplearningandsupervisedmachinelearningmodelforpredictivefetalmonitoring AT stevencorns integrateddeeplearningandsupervisedmachinelearningmodelforpredictivefetalmonitoring |