Ghumre, Yashvi and Lamkoti, Priyanka and Mhatre, Shanti and Jambhale, Pooja (2025) Juno: A Complete Pregnancy Tracker for Advancing Maternal Healthcare and Risk Prediction. International Journal of Innovative Science and Research Technology, 10 (5): 25may323. pp. 1077-1083. ISSN 2456-2165

[thumbnail of IJISRT25MAY323.pdf] Text
IJISRT25MAY323.pdf - Published Version

Download (827kB)

Abstract

Pregnancy is one of the most fascinating and chal-lenging experiences the human body can go through, and it is different for every person, influenced by a wide range of physical, emotional, lifestyle, and social factors. Early detection and prediction of potential complications are essential to im-proving outcomes for both mother and baby. This project uses various machine learning methods on pre-pregnancy data such as physical health, stress levels, and lifestyle choices to predict the risk of pregnancy-related issues. Further, to support the journey, we developed the Juno application—a digital companion that helps expectant mothers document and manage their pregnancy journey from beginning to postpartum. Users can track physical changes, moods, symptoms, supplement intake, medical appoint-ments, and receive useful reminders and suggestions. The app also provides insights into fetal development and aims to offer support throughout childbirth and after delivery. Juno makes it easier to stay organized, reflect on changes, and recognize when to seek help, contributing to better health outcomes. Our goal is to combine technology with care to offer a simple yet effective tool that supports women through one of the most life-changing Experiences.

Item Type: Article
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User: Editor IJISRT Publication
Date Deposited: 05 Jun 2025 11:40
Last Modified: 05 Jun 2025 11:40
URI: https://eprint.ijisrt.org/id/eprint/1058

Actions (login required)

View Item
View Item