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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63975
Volume:
10 |
5 |
May 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Artificial Intelligence and Machine Learning
G H Raisoni College of Engineering and Management
Department of Artificial Intelligence and Machine Learning
G H Raisoni College of Engineering and Management
Department of Artificial Intelligence and Machine Learning
G H Raisoni College of Engineering and Management
Department of Artificial Intelligence and Machine Learning
G H Raisoni College of Engineering and Management
Department of Artificial Intelligence and Machine Learning
G H Raisoni College of Engineering and Management
Department of Artificial Intelligence and Machine Learning
G H Raisoni College of Engineering and Management
Department of Artificial Intelligence and Machine Learning
G H Raisoni College of Engineering and Management
Pregnant women frequently require several applications to attend to their health, appointments and educational resources. This piecemeal strategy leaves people confused and puts them at greater risk of exposure to inaccurate medical information online. To address this challenge, we have created MotherCare, an integrated web platform that integrates key prenatal and postnatal services in one secure system. The personalized health dashboard, appointment booking system and medically reviewed educational content all make for an ideal combination of features at MotherCare. Two smart features are also incorporated into the platform. An AI chatbot with Google Gemini API provides users with rapid and accurate answers to their inquiries. Second, a pregnancy risk prediction model can be used to detect possible complications of pregnancy, such as gestational diabetes and hypertension, early via the maternal health vital. A Gradient Boosting Classifier is used to construct the model, and the model can facilitate proactive maternal care. Built with MERN stack (MongoDB, ExpressJS, ReactJS, and NodeJS), incorporating a microservices architecture with Python, ensuring scalability and security. The prediction model has achieved an accuracy of 92% and a recall of 94% on the test set, with good performance in maternal health monitoring.
KEYWORDS
In this section, the conclusion and future work are to be provided. Our contribution to this paper was the proposal of MotherCare a holistic AI based system which aims at facilitating the maternal care by giving all our users what they require in one place. The three issues that prevail in the contemporary online healthcare environment that our system will address include the lack of consolidated platforms, the misrepresentation of health information, and the lack of active healthcare management. Our risk forecasting model on Gradi- ent Boosting showed success, which implies the usefulness of AI-based innovation in enhancing preventive healthcare services provided in the course of pregnancy. In the future, we have some priorities that we intend to develop. These include: Creation of native mobile applications on the iOS and Android platform - this will make it easier to access our mobile apps, and we will also be able to do push notifications regarding very important health alerts. Integrating our platform with the wearable technology - this will ensure automatic and real-time data entry and will enhance the accuracy and promptness of our predictive capabilities as to risk.
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Publication Date
5/25/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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