Proceedings of the 1st Engineering Data Analytics and Management Conference (EAMCON 2025)

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Early PCOS Detection and Support through a Mobile Application: Integrating Machine Learning and Lifestyle Tracking System

Authors
Aishwarya Sheetalkumar Patil1, Napa Lakshmi1, *, S. B. Kshama1
1School of Computer Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, India
*Corresponding author. Email: napa.lakshmi@manipal.edu
Corresponding Author
Napa Lakshmi
Available Online 31 December 2025.
DOI
10.2991/978-94-6463-978-0_52How to use a DOI?
Keywords
PCOS; Polycystic Ovary Syndrome; Machine Learning; XGBoost; mHealth; Feature Selection; Early Diagnosis; Clinical Decision Support
Abstract

Polycystic Ovary Syndrome (PCOS) is a multifactorial endocrine disorder, which often goes undiagnosed due to its heterogenous presentation and lack of early detection tools. This study explores a machine learning based approach for early PCOS prediction using a publicly available clinical dataset from Kaggle. This dataset contains 39 features related to hormonal levels, lifestyle and physical parameters. The data was preprocessed, label encoded and class balanced using SMOTE, after which five supervised learning models were evaluated. Out of these 5 models the XGBoost classifier achieved the best results – 93.15% accuracy, 0.92 precision and 0.95 recall. To bridge the gap between clinical insights and accessible diagnostics, this paper proposes a mobile health application that integrates the trained model to offer personalized PCOS risk assessment via manual data entry or automated extraction of data from medical reports. The system illustrated the practical potential of combining machine learning with mobile health (mHealth) platforms to enable scalable and early-stage screening for PCOS to support lifestyle-based interventions.

Copyright
© 2025 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 1st Engineering Data Analytics and Management Conference (EAMCON 2025)
Series
Advances in Engineering Research
Publication Date
31 December 2025
ISBN
978-94-6463-978-0
ISSN
2352-5401
DOI
10.2991/978-94-6463-978-0_52How to use a DOI?
Copyright
© 2025 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.

Cite this article

risenwbib
TY  - CONF
AU  - Aishwarya Sheetalkumar Patil
AU  - Napa Lakshmi
AU  - S. B. Kshama
PY  - 2025
DA  - 2025/12/31
TI  - Early PCOS Detection and Support through a Mobile Application: Integrating Machine Learning and Lifestyle Tracking System
BT  - Proceedings of the 1st Engineering Data Analytics and Management Conference (EAMCON 2025)
PB  - Atlantis Press
SP  - 612
EP  - 622
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-978-0_52
DO  - 10.2991/978-94-6463-978-0_52
ID  - Patil2025
ER  -