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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63125
Volume:
10 |
5 |
May 2026
Pages:
1-12
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.
B.Tech
Shri Rawatpura Sarkar University , Raipur
B.Tech
Shri Rawatpura Sarkar University , Raipur
In the modern digital era, managing chronic health conditions such as Polycystic Ovary Syndrome (PCOS) has become increasingly challenging due to lack of awareness, irregular monitoring, and fragmented healthcare solutions. While digital tools are widely available, few are specifically designed to support continuous health tracking and awareness for PCOS management. This research presents the development of a PCOS Health Tracker that integrates full stack web technologies to provide real-time monitoring of symptoms, menstrual cycles, and lifestyle patterns. The system collects user health data, processes it through structured analysis, and delivers meaningful insights through an interactive dash-board designed to improve awareness and self-management. The application architecture combines React.js for the front-end interface, Node.js and Express for backend processing, and MongoDB for data storage. During testing with 20 users, the system achieved an average response time of 1.5 sec-onds and a data tracking accuracy of approximately 92%. Results indicate improved user engagement, better consisten-cy in health tracking, and increased awareness of PCOS-related patterns. The proposed system demonstrates how digital health platforms can enhance personal healthcare management and contribute to user-centric wellness solutions.
KEYWORDS
This research introduced the PCOS Health Tracker, a web-based system designed to support the monitoring and management of Polycystic Ovary Syndrome (PCOS) through digital health technologies. The application enables users to track symptoms, monitor menstrual cycles, and analyze health patterns, helping improve awareness and consistency in managing the condition. Developed using React.js, Node.js, Express, and MongoDB, the system demonstrated efficient performance with an average response time of approximately 1.5 seconds and a data tracking accuracy of around 92% during testing. User feedback indicated improved understanding of symptom patterns, better engagement in daily tracking, and increased awareness of PCOS-related health factors compared to traditional methods. The project highlights how modern web technologies can be leveraged to enhance personal healthcare management and provide accessible, user-friendly solutions for chronic conditions. It emphasizes that digital platforms can play a crucial role in bridging the gap between health awareness and practical self-management. Future enhancements such as AI-based prediction models, chatbot integration, personalized health recommendations, and mobile application development can further improve system effectiveness and user experience. This work establishes a strong foundation for future research in digital health monitoring systems and user-centric healthcare solutions.
1. Zeng, H., et al. (2026). Effectiveness of digital health interventions in women with PCOS. 2. Wright, P. J., et al. (2025). Digital technology use among women with PCOS. 3. Harrath, A. H., et al. (2026). Modern PCOS management and advancements. 4. Spandana, K., et al. (2025). E-healthcare platforms for PCOS management. 5. Wang, J., et al. (2025). Artificial intelligence in PCOS management. 6. Li, M., et al. (2025). Intelligent detection framework for PCOS. 7. CEHRes Framework Study (2026). Digital health systems for PCOS. 8. Gao, X., et al. (2025). Data-driven subtypes in PCOS using analytics. 9. Khan, A. S., et al. (2025). Explainable AI for PCOS risk prediction. 10. Hoque, M., et al. (2026). Deep learning models for PCOS detection.
Publication Date
5/13/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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