Prediction of Fetal Health Status Using Machine Learning

Naidile S, Saragodu and Shreedhara N, Hedge and Harprith, Kaur (2024) Prediction of Fetal Health Status Using Machine Learning. Journal of Data Science, 2024 (17). pp. 1-7. ISSN 2805-5160

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Abstract

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

Item Type:	Article
Uncontrolled Keywords:	SVM, LR, Random Forest Classification, Fetal Health, Machine Learning
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software R Medicine > R Medicine (General)
Depositing User:	Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited:	23 Jul 2024 06:55
Last Modified:	23 Jul 2024 06:55
URI:	http://eprints.intimal.edu.my/id/eprint/1944

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