Machine Learning-Enabled Menstrual Cycle Prediction and Analytics in a Secure Web-Based Platform

Yogesh Pawar; Apurva Gangrade; Archita Tiwari; Shivprasad Chinchole; Vaibhav Chandwalker

doi:10.2991/978-94-6239-723-1_43

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Machine Learning-Enabled Menstrual Cycle Prediction and Analytics in a Secure Web-Based Platform

Authors

Yogesh Pawar¹^{, *}, Apurva Gangrade¹, Archita Tiwari¹, Shivprasad Chinchole¹, Vaibhav Chandwalker¹

¹Department of CSE– Internet of Things and Cybersecurity including Blockchain Technology, Vishwakarma Institute of Technology, Pune, Maharashtra, India

^*Corresponding author. Email: yogesh.pawar@vit.edu

Corresponding Author

Yogesh Pawar

Available Online 14 July 2026.

DOI: 10.2991/978-94-6239-723-1_43 How to use a DOI?
Keywords: Menstrual Cycle Tracking; Predictive Modeling; Machine Learning; Mobile Health (mHealth); Self-Tracked Data; Web-Based Health System
Abstract: Menstrual health tracking is a significant aspect of digital health, and women use various apps for menstrual cycle prediction. However, these apps do not provide accurate prediction and analytics [2]. To solve this problem, a web-based platform named CycleCare is developed. This platform uses various data analytics and prediction techniques. According to research, physiological variables and self-tracked data are useful in improving menstrual health analysis [1, 3, 9]. In the proposed system, generative and time-series models are used for predicting menstrual cycle lengths and phase changes. These models can handle irregular menstrual cycles and missing data [7, 10, 11]. Furthermore, the proposed system is developed considering user engagement, user experience, and data privacy, making it a health application [5, 6, 8].
Copyright: © 2026 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the International Conference on Responsible, Risk-aware, and Regulated AI (RRRAI 2026)
Series: Advances in Intelligent Systems Research
Publication Date: 14 July 2026
ISBN: 978-94-6239-723-1
ISSN: 1951-6851
DOI: 10.2991/978-94-6239-723-1_43 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Yogesh Pawar
AU  - Apurva Gangrade
AU  - Archita Tiwari
AU  - Shivprasad Chinchole
AU  - Vaibhav Chandwalker
PY  - 2026
DA  - 2026/07/14
TI  - Machine Learning-Enabled Menstrual Cycle Prediction and Analytics in a Secure Web-Based Platform
BT  - Proceedings of the International Conference on Responsible, Risk-aware, and Regulated AI (RRRAI 2026)
PB  - Atlantis Press
SP  - 485
EP  - 495
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-723-1_43
DO  - 10.2991/978-94-6239-723-1_43
ID  - Pawar2026
ER  -

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