Meenal Kamlakar¹* and Dipti D Patil²

¹Department of Computer Engineering, Savitribai Phule Pune University, India
²Department of Information Technology, MKSSS’s Cummins College of Engineering for women, Pune

*Corresponding Author: Meenal Kamlakar, Department of Computer Engineering, Savitribai Phule Pune University, India.

Received: October 21, 2021; Published: November 10, 2021

Abstract

The experience of pregnancy and birthing is different for every mother and her baby. Unfortunately this experience may not always be very smooth due to various complications that may occur during delivering the baby. Premature babies even if kept in neonatal care are susceptible to a lot of hazardous health conditions. Underdeveloped brain and lungs, smaller or weaker babies resulting in feeding inabilities and being prone to diseases, are a few problems babies may face if born early. Also mothers can face problems due to high blood pressure, gestational or preexisting diabetes, infection or other medical conditions. Some mothers may face problems if induced with labour before time such as rupturing of placenta, and increased chances of C section [16].

There is thus a need to identify or if possible predict the risk associated with pregnancy and the mode of delivery. Different medical indicators or parameters can be used for monitoring pregnancy, predicting delivery time and mode of delivery [16]. These can be studied and can be analyzed using machine learning algorithms. The analysis can be used to predict the appropriate time and mode of childbirth along with the risks and it will be less error-prone. To support this wearable sensor device which senses abnormalities can be used remotely to monitor the patients health and take necessary actions in case of emergencies. The devices worn during delivery time can indicate or predict abnormalities while in labour to help prevent unfortunate events.

Keywords: Lactic Acid; Pregnancy; MRI Scans

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Citation

Citation: Meenal Kamlakar and Dipti D Patil. “Use of Machine Learning and Sensors for Monitoring Pregnancy". Acta Scientific Computer Sciences 3.12 (2021): 38-41.

Copyright

Copyright: © 2021 Meenal Kamlakar and Dipti D Patil. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.