Comparative Analysis of Different Efficient Machine Learning Methods for Fetal Health Classification
Obstetricians often utilize cardiotocography (CTG) to assess a child’s physical health throughout pregnancy because it gives data on the fetal heartbeat and uterine contractions, which helps identify whether the fetus is pathologic or not. Obstetricians have traditionally analyzed CTG data artificia...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Applied Bionics and Biomechanics |
| Online Access: | http://dx.doi.org/10.1155/2022/6321884 |
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| author | Md Takbir Alam Md Ashibul Islam Khan Nahian Nakiba Dola Tahia Tazin Mohammad Monirujjaman Khan Amani Abdulrahman Albraikan Faris A. Almalki |
| author_facet | Md Takbir Alam Md Ashibul Islam Khan Nahian Nakiba Dola Tahia Tazin Mohammad Monirujjaman Khan Amani Abdulrahman Albraikan Faris A. Almalki |
| author_sort | Md Takbir Alam |
| collection | DOAJ |
| description | Obstetricians often utilize cardiotocography (CTG) to assess a child’s physical health throughout pregnancy because it gives data on the fetal heartbeat and uterine contractions, which helps identify whether the fetus is pathologic or not. Obstetricians have traditionally analyzed CTG data artificially, which takes time and is unreliable. As a result, creating a fetal health classification model is essential, as it may save not only time but also medical resources in the diagnosis process. Machine learning (ML) is currently extensively used in fields such as biology and medicine to address a variety of issues, due to its fast advancement. This research covers the findings and analyses of multiple machine learning models for fetal health classification. The method was developed using the open-access cardiotocography dataset. Although the dataset is modest, it contains some noteworthy values. The data was examined and used in a variety of ML models. For classification, random forest (RF), logistic regression, decision tree (DT), support vector classifier, voting classifier, and K-nearest neighbor were utilized. When the results are compared, it is discovered that the random forest model produces the best results. It achieves 97.51% accuracy, which is better than the previous method reported. |
| format | Article |
| id | doaj-art-b6034d9294ea4098a44f2c33b0783cde |
| institution | OA Journals |
| issn | 1754-2103 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Bionics and Biomechanics |
| spelling | doaj-art-b6034d9294ea4098a44f2c33b0783cde2025-08-20T02:23:23ZengWileyApplied Bionics and Biomechanics1754-21032022-01-01202210.1155/2022/6321884Comparative Analysis of Different Efficient Machine Learning Methods for Fetal Health ClassificationMd Takbir Alam0Md Ashibul Islam Khan1Nahian Nakiba Dola2Tahia Tazin3Mohammad Monirujjaman Khan4Amani Abdulrahman Albraikan5Faris A. Almalki6Department of Electrical and Computer EngineeringDepartment of Electrical and Computer EngineeringDepartment of Electrical and Computer EngineeringDepartment of Electrical and Computer EngineeringDepartment of Electrical and Computer EngineeringDepartment of Computer ScienceDepartment of Computer EngineeringObstetricians often utilize cardiotocography (CTG) to assess a child’s physical health throughout pregnancy because it gives data on the fetal heartbeat and uterine contractions, which helps identify whether the fetus is pathologic or not. Obstetricians have traditionally analyzed CTG data artificially, which takes time and is unreliable. As a result, creating a fetal health classification model is essential, as it may save not only time but also medical resources in the diagnosis process. Machine learning (ML) is currently extensively used in fields such as biology and medicine to address a variety of issues, due to its fast advancement. This research covers the findings and analyses of multiple machine learning models for fetal health classification. The method was developed using the open-access cardiotocography dataset. Although the dataset is modest, it contains some noteworthy values. The data was examined and used in a variety of ML models. For classification, random forest (RF), logistic regression, decision tree (DT), support vector classifier, voting classifier, and K-nearest neighbor were utilized. When the results are compared, it is discovered that the random forest model produces the best results. It achieves 97.51% accuracy, which is better than the previous method reported.http://dx.doi.org/10.1155/2022/6321884 |
| spellingShingle | Md Takbir Alam Md Ashibul Islam Khan Nahian Nakiba Dola Tahia Tazin Mohammad Monirujjaman Khan Amani Abdulrahman Albraikan Faris A. Almalki Comparative Analysis of Different Efficient Machine Learning Methods for Fetal Health Classification Applied Bionics and Biomechanics |
| title | Comparative Analysis of Different Efficient Machine Learning Methods for Fetal Health Classification |
| title_full | Comparative Analysis of Different Efficient Machine Learning Methods for Fetal Health Classification |
| title_fullStr | Comparative Analysis of Different Efficient Machine Learning Methods for Fetal Health Classification |
| title_full_unstemmed | Comparative Analysis of Different Efficient Machine Learning Methods for Fetal Health Classification |
| title_short | Comparative Analysis of Different Efficient Machine Learning Methods for Fetal Health Classification |
| title_sort | comparative analysis of different efficient machine learning methods for fetal health classification |
| url | http://dx.doi.org/10.1155/2022/6321884 |
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