Prediction of Fetal Health Status Using Machine Learning
The goal of this promising area of study is to enhance prenatal care and lower fetal morbidity and mortality by utilizing machine learning to anticipate fetal disease. In this study, we present a machine learning-based strategy for predicting fetal diseases from clinical data. First, we gathered a s...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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INTI International University
2024
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| Online Access: | http://eprints.intimal.edu.my/1944/ http://eprints.intimal.edu.my/1944/1/488 |
| _version_ | 1848766878502092800 |
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| author | Naidile S, Saragodu Shreedhara N, Hegde Harprith, Kaur |
| author_facet | Naidile S, Saragodu Shreedhara N, Hegde Harprith, Kaur |
| author_sort | Naidile S, Saragodu |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | The goal of this promising area of study is to enhance prenatal care and lower fetal morbidity and mortality by utilizing machine learning to anticipate fetal disease. In this study, we present a machine learning-based strategy for predicting fetal diseases from clinical data. First, we gathered a sizable collection of clinical information from expectant mothers with various fetal disorders. Using clinical guidelines, we pre-processed the data and retrieved pertinent features. We integrated a range of machine learning algorithms, including logistic regression, support vector machines, decision trees, and random forests, to train and test our model. We evaluated the performance of our model using several factors, such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). The results of this study demonstrate how machine learning algorithms can accurately forecast fetal health status. The developed models achieve good accuracy and AUC-ROC ratings to distinguish between healthy and at-risk fetuses. The interpretability study identifies key clinical characteristics that have a significant impact on the prediction, providing medical practitioners with useful information when making decisions about prenatal care. Through the provision of more unbiased and precise assessments of fetal health status, machine learning techniques incorporated into prenatal care have the potential to transform the industry. By providing accurate and early projections, this technology can assist healthcare professionals in identifying high-risk pregnancies and carrying out the necessary procedures, improving mother and fetal outcomes. Future research should concentrate on verifying and improving predictive models on larger and more varied datasets to ensure real-world applicability and reliability |
| first_indexed | 2025-11-14T11:58:09Z |
| format | Article |
| id | intimal-1944 |
| institution | INTI International University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:58:09Z |
| publishDate | 2024 |
| publisher | INTI International University |
| recordtype | eprints |
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| spelling | intimal-19442024-08-06T06:19:58Z http://eprints.intimal.edu.my/1944/ Prediction of Fetal Health Status Using Machine Learning Naidile S, Saragodu Shreedhara N, Hegde Harprith, Kaur QA75 Electronic computers. Computer science QA76 Computer software R Medicine (General) The goal of this promising area of study is to enhance prenatal care and lower fetal morbidity and mortality by utilizing machine learning to anticipate fetal disease. In this study, we present a machine learning-based strategy for predicting fetal diseases from clinical data. First, we gathered a sizable collection of clinical information from expectant mothers with various fetal disorders. Using clinical guidelines, we pre-processed the data and retrieved pertinent features. We integrated a range of machine learning algorithms, including logistic regression, support vector machines, decision trees, and random forests, to train and test our model. We evaluated the performance of our model using several factors, such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). The results of this study demonstrate how machine learning algorithms can accurately forecast fetal health status. The developed models achieve good accuracy and AUC-ROC ratings to distinguish between healthy and at-risk fetuses. The interpretability study identifies key clinical characteristics that have a significant impact on the prediction, providing medical practitioners with useful information when making decisions about prenatal care. Through the provision of more unbiased and precise assessments of fetal health status, machine learning techniques incorporated into prenatal care have the potential to transform the industry. By providing accurate and early projections, this technology can assist healthcare professionals in identifying high-risk pregnancies and carrying out the necessary procedures, improving mother and fetal outcomes. Future research should concentrate on verifying and improving predictive models on larger and more varied datasets to ensure real-world applicability and reliability INTI International University 2024-07 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/1944/1/488 Naidile S, Saragodu and Shreedhara N, Hegde and Harprith, Kaur (2024) Prediction of Fetal Health Status Using Machine Learning. Journal of Data Science, 2024 (17). pp. 1-7. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software R Medicine (General) Naidile S, Saragodu Shreedhara N, Hegde Harprith, Kaur Prediction of Fetal Health Status Using Machine Learning |
| title | Prediction of Fetal Health Status Using Machine Learning |
| title_full | Prediction of Fetal Health Status Using Machine Learning |
| title_fullStr | Prediction of Fetal Health Status Using Machine Learning |
| title_full_unstemmed | Prediction of Fetal Health Status Using Machine Learning |
| title_short | Prediction of Fetal Health Status Using Machine Learning |
| title_sort | prediction of fetal health status using machine learning |
| topic | QA75 Electronic computers. Computer science QA76 Computer software R Medicine (General) |
| url | http://eprints.intimal.edu.my/1944/ http://eprints.intimal.edu.my/1944/ http://eprints.intimal.edu.my/1944/1/488 |