Statistical Insights Into Machine Learning-Based Box Models for Pregnancy Care and Maternal Mortality Reduction: A Literature Survey
Maternal mortality is a major public health concern worldwide. It is the number of preventable deaths that occur each year due to pregnancy and childbirth. The research investigates how machine learning may be used to minimize maternal mortality. Historical data on maternal health is used to develop...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10529290/ |
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| author | Issac Neha Margret K. Rajakumar K. V. Arulalan S. Manikandan Valentina |
| author_facet | Issac Neha Margret K. Rajakumar K. V. Arulalan S. Manikandan Valentina |
| author_sort | Issac Neha Margret |
| collection | DOAJ |
| description | Maternal mortality is a major public health concern worldwide. It is the number of preventable deaths that occur each year due to pregnancy and childbirth. The research investigates how machine learning may be used to minimize maternal mortality. Historical data on maternal health is used to develop predictive models, early detection systems, and resource allocation techniques. Machine learning helps to identify risk factors, monitor vital signs, and improve access to care. This allows for targeted interventions and better healthcare delivery. The challenges of data accessibility and model interpretation are addressed, highlighting the ethical and equitable applications of machine learning in maternal healthcare. This study emphasizes the potential of machine learning to reduce maternal mortality rates and the pressing need for its incorporation into healthcare systems worldwide. |
| format | Article |
| id | doaj-art-3fadc546163549b485be14ddd33d58c4 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3fadc546163549b485be14ddd33d58c42025-01-25T00:00:26ZengIEEEIEEE Access2169-35362024-01-0112681846820710.1109/ACCESS.2024.339982710529290Statistical Insights Into Machine Learning-Based Box Models for Pregnancy Care and Maternal Mortality Reduction: A Literature SurveyIssac Neha Margret0K. Rajakumar1https://orcid.org/0000-0001-6614-8647K. V. Arulalan2S. Manikandan3 Valentina4https://orcid.org/0000-0003-0885-1283School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaA. A. Child Care Clinic, Vellore, Tamil Nadu, IndiaDRDO, Bengaluru, Karnataka, IndiaDepartment of Automation and Applied Informatics, University of Arad, Arad, RomaniaMaternal mortality is a major public health concern worldwide. It is the number of preventable deaths that occur each year due to pregnancy and childbirth. The research investigates how machine learning may be used to minimize maternal mortality. Historical data on maternal health is used to develop predictive models, early detection systems, and resource allocation techniques. Machine learning helps to identify risk factors, monitor vital signs, and improve access to care. This allows for targeted interventions and better healthcare delivery. The challenges of data accessibility and model interpretation are addressed, highlighting the ethical and equitable applications of machine learning in maternal healthcare. This study emphasizes the potential of machine learning to reduce maternal mortality rates and the pressing need for its incorporation into healthcare systems worldwide.https://ieeexplore.ieee.org/document/10529290/Artificial intelligencebox modelsmachine learning algorithmsmaternal mortalitypregnancy care |
| spellingShingle | Issac Neha Margret K. Rajakumar K. V. Arulalan S. Manikandan Valentina Statistical Insights Into Machine Learning-Based Box Models for Pregnancy Care and Maternal Mortality Reduction: A Literature Survey IEEE Access Artificial intelligence box models machine learning algorithms maternal mortality pregnancy care |
| title | Statistical Insights Into Machine Learning-Based Box Models for Pregnancy Care and Maternal Mortality Reduction: A Literature Survey |
| title_full | Statistical Insights Into Machine Learning-Based Box Models for Pregnancy Care and Maternal Mortality Reduction: A Literature Survey |
| title_fullStr | Statistical Insights Into Machine Learning-Based Box Models for Pregnancy Care and Maternal Mortality Reduction: A Literature Survey |
| title_full_unstemmed | Statistical Insights Into Machine Learning-Based Box Models for Pregnancy Care and Maternal Mortality Reduction: A Literature Survey |
| title_short | Statistical Insights Into Machine Learning-Based Box Models for Pregnancy Care and Maternal Mortality Reduction: A Literature Survey |
| title_sort | statistical insights into machine learning based box models for pregnancy care and maternal mortality reduction a literature survey |
| topic | Artificial intelligence box models machine learning algorithms maternal mortality pregnancy care |
| url | https://ieeexplore.ieee.org/document/10529290/ |
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