A Systematic Review using Machine Learning Algorithms for Predicting Preterm Birth

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2022 by IJETT Journal
Volume-70 Issue-5
Year of Publication : 2022
Authors : Dr. R. Surendiran, R. Aarthi, M. Thangamani, S. Sugavanam, R. Sarumathy
DOI :  10.14445/22315381/IJETT-V70I5P207


MLA Style: Dr. Surendiran, R., et al. "A Systematic Review using Machine Learning Algorithms for Predicting Preterm Birth." International Journal of Engineering Trends and Technology, vol. 70, no. 5, May. 2022, pp. 46-59. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I5P207

APA Style:Dr. Surendiran, R., Aarthi, R., Thangamani, M., Sugavanam, S., Sarumathy, R.. (2022). A Systematic Review using Machine Learning Algorithms for Predicting Preterm Birth. International Journal of Engineering Trends and Technology, 70(5), 46-59. https://doi.org/10.14445/22315381/IJETT-V70I5P207

Preterm births (PTB) affect nearly 15 million kids worldwide. At present, medical fields aim to reduce the possessions of prematurity rather than avoid it. The cervix is currently measured during a transvaginal ultrasound, used to diagnose the condition. Because of the complexities of this process, preterm births cannot be accurately predicted. Machine learning is becoming more popular for prediction and diagnosis in health care. This study looks at how artificial intelligence can predict preterm labor and birth. According to this study, various machine learning approaches can aid in the diagnosis of preterm births. In terms of predicting preterm birth, machine learning can be well suited for various data types. Electro hysterogram signals, electronic health records, and transvaginal ultrasounds are examples. This review`s goal remains to summarize machine learning procedures intended for predicting premature birth.

Prediction, Preterm birth, Artificial Intelligence, Machine learning.

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